Electroencephalography based systems and methods for selecting therapies and predicting outcomes

ABSTRACT

A method and system for utilizing neurophysiologic information obtained by techniques such as quantitative electroencephalography (QEEG), electrode recordings, MRI in appropriately matching patients with therapeutic entities is disclosed. The present invention enables utilization of neurophysiologic information, notwithstanding its weak correlation with extant diagnostic schemes for mental disorders, for safer and expeditious treatment for mental disorders, discovering new applications for therapeutic entities, improved testing of candidate therapeutic entities, inferring the presence or absence of a desirable response to a treatment, and deducing the mode of action of one or more therapeutic entities. In particular, methods for effectively comparing neurophysiologic information relative to a reference set are disclosed along with database-based tools for deducing therapeutic entity actions on particular patients such that these tools are readily accessible to remote users.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the U.S. patent application Ser. No.10/193,735 filed on Jul. 11, 2002 and U.S. Provisional Application Nos.60/304,628 filed on Jul. 11, 2001, and the U.S. patent application Ser.No. 09/148,591, now abandoned, filed on Sep. 4, 1998, and the publishedPCT application NO. PCT/US01/04148 filed on Feb. 9, 2001; and claimspriority of U.S. Provisional Patent Application No. 60/304,627 filed onJul. 11, 2001 and of U.S. patent application Ser. Nos. 09/501,149 and09/930,632 filed on Feb. 9, 2000, and Aug. 15, 2001 respectively, whichare all incorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The present invention relates to the field of electroencephalography(EEG), and more specifically includes methods and systems for selectingtherapies for behaviorally-diagnosed psychiatric conditions and forpredicting outcomes from therapies. This invention also includes methodsof treating patients with the selected therapies.

BACKGROUND OF THE INVENTION

Conventional treatment for mental disorders follows a diagnosis inaccordance with a standard followed by selection of a treatment reportedto be effective for that particular diagnosis. Typically there areseveral treatment options available. The selection of a particulartreatment depends on the judgement of a physician. The soundness of thisjudgement, in turn, depends on the information available to thephysician. The information available to the physician often includesrisk of allergic responses and the like in the event a substance isadministered as part of the treatment. However, little else is at handto help the physician avoid prescribing a treatment to which the patientis non-responsive or worse, a treatment that aggravates the mentalillness rather than control it. Thus, physicians attempt numeroustreatment modalities in order to determine an effective treatment in agiven case.

Heterogeneity of treatment response of diagnosed mental illness is wellknown. Accordingly, there have been attempts to improve the diagnosticmethods to identify more homogeneously responsive groupings ofparticular mental disorders. Yet, despite the increased homogeneity ofdiagnosed mental illness within and across practitioners, response totreatment of mental disorders continues to be markedly heterogeneous.

Presently, the Diagnostic and Statistical Manual of Mental Disorders(“DSM”) provides definitive guidelines for diagnosing and treatingmental disorders. See, e.g., Nathan et al.: “Psychopathology:Description and Classification” in Annual Reviews of Psychology,50:79-107 (1999). The DSM manual, presently in its fourth edition,commonly referred to as “DSM-IV,” is organized along various axes. Forinstance, axis I disorders include major depression and schizophrenia;axis II includes personality disorders; while axis III addressesphysical disorders contributing to psychological symptoms. A convenientview of the DSM entries is in accordance with its chapters since theyare topically organized to avoid excessive details. Such details arewithin the plurality of diagnoses described in each of the chapters.Example chapters include those on ‘childhood disorders,’ ‘eatingdisorders,’ ‘substance-related disorders,’ ‘anxiety,’ ‘mood disorders’and the like.

Another, alternative standard for diagnosing mental disorders is the setof criteria maintained by the World Health Organization (“WHO”) as theInternational Classification of Diseases (“ICD”). ICD is employed moreextensively in Europe than North America, although, DSM-IV remains thepredominant international standard for allowing independent healthproviders to make similar diagnoses of a particular patient despite theinherently subjective nature of the underlying observations.

Applying the aforesaid standard diagnostic techniques requires datacollection. At present there are available various methods of datacollection, such as objective measures of brain activity or patientinterviews and observations of subject's stimulated or natural behavior.For instance, objective measures such as recordings from the electrodesattached to the head of a subject, termed electroencephalograms (“EEG”),have long been available. However, they have had very limited useoutside the context of monitoring and controlling seizures or studyingsleep related disorders.

Notably, known systems for diagnosing mental disorders, such as DSM-UV,do not employ EEG recordings to aid in either diagnosis or treatment ofa mental disorder other than in the context of seizures, brain death,intraoperative monitoring or dementia. For instance, a committee ofexperts in an article, Hoffman et al., J. of Neuropsychiatry andClinical Neurosciences, 11:3 (1999), cites the American Academy ofNeurology (“AAN”) as recommending quantitative EEG (“QEEG”) as being ofno clinical value in 1987 and in 1997 as being of limited clinical usein (a) stroke, (b) dementia, (c) intraoperative monitoring, and (d)epilepsy. However, clinical utility was not accepted by AAN forapplication in (a) traumatic brain injury, (b) psychiatric disordersincluding learning disabilities, and (c) medical-legal use. WhileHoffman et al. disagree with the AAN's limited recommendations for useof QEEG, they do not offer concrete alternatives for therapeuticapplication of QEEG in treating mental disorders. This is illustrativeof the challenges posed by objective data such asneurometric/neurophysiologic information in general and EEG data inparticular in treating mental disorders.

The neurophysiologic technique of EEG measures the electrical activityof the brain as a function of time varying spontaneous potentials (SP)through a number of electrodes placed at standard locations on thescalp. The neurophysiologic information obtained through EEG analysis isrecorded as sets of traces of the amplitude of SP over time for scalpelectrodes that are variably referenced. This analog EEG information canthen be visually analyzed and interpreted for signal abnormalities.

In the 1970's, quantitative analysis of the EEG signal provided rapideasy access to measurements that extended the EEG method beyondqualitative visual detection of signal abnormality. Quantitative EEG(QEEG) studies involve the multi-channel acquisition, processing, andanalysis of brain activity often but not exclusively by computers. Anexample of an EEG/QEEG instrument is the Easy Writer II system,available from Caldwell Laboratories, Inc. (Kennewick, Wash.).

In one version of EEG/QEEG recordings electrodes (at least oneelectrode, preferably nineteen electrodes and most preferably 21electrodes) are commonly placed at standard locations on the scalp usingthe International 10/20 Placement System. A multi-channel recording ofthe brain's activity in an alert, awake, eyes-closed, or “background”state is then recorded and analyzed often by use of Fast FourierTransform (FFT) signal processing. FFT processing of the raw EEG permitsmeasurement and quantification of multiple characteristics of brainelectrical activity. In this process, optionally, signals due to muscleor eye movement or environmental noise are rejected, leaving informationrelated to neurophysiology for further analysis.

EEG recordings are typically of uncertain quality and often require theaid of an experienced technician. See, e.g., Nuwer, Marc, “Assessment ofdigital EEG, quantitative EEG, and EEG brain mapping: Report of theAmerican Academy of Neurology and the American Clinical NeurophysiologySociety” in Neurology, 49:277-292 at 279 (1997). Still, there are knownmethods for obtaining EEG data reliably by placing electrodes(satisfying specified impedance limits) relative to well-definedlandmarks on the skull such as the International 10/20 system. U.S. Pat.No. 5,730,146 issued to Itil et al. on Mar. 24, 1998 discloses anapparatus for reproducibly placing electrodes, in accordance with theInternational 10/20 system, on the head of a subject and transmittingEEG data to a remote location over a telephone connection. U.S. Pat. No.5,816,247 issued to Douglas E. Maynard on Oct. 6, 1998 discloses anapparatus and method for collecting EEG signals from a subject andsubjecting the signals to sorting with the aid of a suitably trainedneural network.

Not everyone with an abnormal EEG has an associated disorder—mental orotherwise. While EEG reveals gross changes such as spikes anddisturbances accompanying seizures or the lack of brain activityassociated with death, it is less than successful in providing acorrelation with known mental disorders as defined by DSM-UV or itsother editions. Similar difficulties are associated with correlatingEEG/QEEG findings with other mental disorder diagnosis systems, such asthe ICD.

DSM-IV manual has many detractors who disagree with variousmethodological details or conclusions therein as well as the basicstrategy underlying the manual. However, in view of the reality ofmental disorders and the therapeutic benefit possible withadministration of substances and therapy to a subject to treat mentaldisorders such criticism does not provide practical alternatives toprescribing substances or treatment other than DSM-IV or a comparablediagnostic scheme. The previously mentioned lack of reliance on EEGrecordings in making diagnosis reflects the lack of correlation betweena diagnosis in accordance with the known systems for diagnosing mentaldisorders, such as DSM-IV, and EEG recordings. In the few instances whenthere is possible a correlation, such as advanced schizophrenia, thereare obvious overt disease indicators that eliminate the need for EEGrecordings in view of the added expense and technical demands made byEEG.

In addition to EEG, objective measures of brain activity includetechniques such as magnetic resonance imaging (MRI), functional magneticresonance imaging (FMRI), positron emission tomography (PET), singlephoton emission computerized tomography (SPECT), magnetoencephalography(MEG), quantitative magnetoencephalography (QMEG) and many others. Allof these techniques are of limited significance in actual treatment ofmental disorders for reasons similar to those discussed in the case ofEEG recordings or cost issues or due to ease of use or a combinationthereof.

Consequently, known attempts at integrating neurophysiologic informationwith treatment start with a definitive DSM, or similar, diagnosisfollowed by an attempt to identify variations in QEEG or EEG thatcorrelate with the known diagnosis. An example of such an approach inthe context of a diagnosis of chronic fatigue syndrome is provided bythe U.S. Pat. No. 5,267,570 issued to Myra S. Preston on Dec. 7, 1993for a “Method of Diagnosing and Treating Chronic Fatigue Syndrome.”Similarly, in the context of a diagnosis of Alzheimer's dementia use ofEEG data is disclosed by the U.S. Pat. No. 5,230,346 issued to Leuchteret al. on Jul. 27, 1993 for “Diagnosing Brain Conditions by QuantitativeElectroencephalography.” Another U.S. Pat. No. 5,873,823 issued toDavid-Eidelberg on Feb. 23, 1999 discloses a more generalized approachto detect markers to aid in screening patients for traditional diagnosisand treatment. The U.S. Pat. No. 5,083,571 granted to Leslie S. Prichepon Jan. 28, 1992 discloses discriminant and cluster analysis of EEG datain diagnosing mental disorders.

None of the aforementioned patents teaches integration of behavioraldefinitions of psychiatric disorders with objective data in view of theresponse of a subject to treatment of the mental state of the patientindependent of the diagnosis. Instead, they focus on refining thediagnosis of traditional behavioral psychiatric disorders with the aidof objective data.

It is not unusual for a therapeutic entity prescribed for a particularmental disorder to entirely fail to alleviate the symptoms or to evenresult in additional or different symptoms. In other words, in additionto weak correlation between traditional diagnostic systems and objectivedata, the correlation between traditional diagnosis and treatments isalso significantly less than desirable.

The absence of a strong correlation between objective data collectedfrom a subject and the known analytic techniques, such as DSM-IV, makesit difficult to discover and utilize the likely utility of a givensubstance or therapy upon administration to a subject. Indeed,identifying a subject as having an abnormal neurological profile needs amore objective basis than that afforded by subjective data to reduceerrors in treatment and improve the likelihood of a successful outcomefor a course of treatment.

Moreover, many known substances and currently available therapeuticentities have yet unknown useful effects on the mental state. Relianceon more subjective observational data based on narrated case history orobservations often masks useful properties of many known substances.Often, in providing information to modify behavior it is difficult toprospectively persuade a subject that the risk of harm or addiction isgreater in the subject's case compared to the general population. Thus,the generation of neurophysiologic information provides a useful toolfor designing and implementing outreach programs.

Some substances are of considerable social and political import sincethe users of such substances are a very small fraction of the generalpopulation, and consequently their needs are easily overshadowed by thecost of servicing and locating such users. While the present lawsencourage such users through provisions such as identifying “orphandrugs” for special treatment, the cost of identifying even the conditionto be targeted by a putative orphan drug poses a challenge. Betteridentification of orphan drugs would not only improve treatmentavailability, but actually provide customized treatment to a widespectrum of subjects.

Moreover, additional substances have addiction associated with theiradministration. Examples include nicotine, typically self-administeredby inhaling fumes, and many other substances whose sale is restricted orprohibited by law. However, educating the public to the dangers posed bysuch substances is difficult in the absence of a customized riskassessment of deleterious responses and the propensity to exhibitaddiction. Presently, there is no method or system for providing suchcustomized yet prospective information as part of public educationcampaigns and preventive care.

The aforementioned shortcomings are overcome by the present invention,described below, in addition to new capabilities enabled in its variousembodiments.

SUMMARY OF THE INVENTION

The invention provides a system and method for choosing a treatmentindependent of a diagnosis based on a treatment-response database ofresponses to treatment. Evaluation of a subject includes obtainingneurophysiologic information in an initial state of the subject.Active-treatment neurological information of the subject is, then,obtained along with an evaluation of whether the subject exhibitedimprovement, non-responsiveness or adverse reactions to the treatment.Statistical techniques isolate factors in the initial state shared by agroup of subjects exhibiting similar responses in a treatment-responsedatabase of responses from several subjects.

Searching this treatment-response database to find treatments associatedwith a desirable response in a subject having a particular initialneurophysiologic state enables evaluation of the likely effect of aproposed treatment on a subject with concomitant reduction inunnecessary experimentation.

Active-treatment neurological information coupled with pretreatmentand/or initial state neurological information is also useful indrug-abuse programs by identifying candidates for adverse effects oftherapeutic entity. These candidates can then be provided individuallytailored information prior to actually experiencing the full range ofthe adverse effects as an effective and specific warning of theconsequences resulting from drug abuse.

The techniques for building the treatment-response database are extendedto enable, for instance, discovering if a particular therapeutic entityhaving failed to exhibit a positive outcome in testing is neverthelesseffective in a smaller subset of patients.

Similarly, design of clinical trials is improved by selection of a setof subjects most likely to respond in a desirable manner to a proposedtherapeutic entity. This both lowers the development costs and makes thetesting safer with superior guidelines for actual clinical use of thecandidate therapeutic entity.

In still another aspect of the invention objective data is furtherapplied to discover new candidate therapeutic entities and new uses forknown therapeutic entities. Moreover, a subject and a method oftreatment are matched objectively to reduce the likelihood ofdeleterious or undesired side effects due to treatment in clinicalpractice or clinical trials. Furthermore, the embodiment of theinvention includes designing clinical trials with a better defined setof subjects to increase the likelihood of discovering both thebeneficial and deleterious side effects of a therapeutic entity alongwith an analytic frame work to identify and correct for non-responsivesubjects.

Thus, a therapeutic entity deemed to have marginal efficacy on anundefined pool of subjects is evaluated for its effect on subjects whocan be differentiated with the aid of prospective and/or retrospectiveanalysis to determine whether they are likely to be responsive,adversely affected or non-responsive. This, in turn, enables better useof a candidate therapeutic entity in actual treatment subsequent to theclinical trials by identifying condition precedent for successful use ofthe therapeutic entity in clinical practice.

In another aspect, the invention enables screening subjects for a commonresponse to a treatment as indicated by neurophysiologic information.Such patients, then are an enriched set for identifying a commonunderlying mechanism at the molecular level and genetic level. Inparticular, shared family history for a particular response pattern toone or more therapeutic entities enables identification of commongenetic determinants underlying the response to the treatment.

In another aspect the invention discloses techniques for constructionand maintenance of useful databases for making treatment recommendationsfor modulating brain function.

In still another aspect, the present invention enables remote assessmentand treatment of physiologic brain imbalances using objective data suchas quantified neurophysiologic information: The treatment-responsedatabase enabled by the invention can be accessed either directly orfrom a remote location, thus providing high quality information topracticing physicians via electronic or wireless links as well.

The invention further provides effective user-interfaces, portabledevices, computer software, computer programming techniques, andalgorithms for conducting the neurophysiologic analysis, remotetransmission, and treatment methods described herein.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic of a treatment response database taught by theinvention;

FIG. 2 illustrates an exemplary method for using a treatment-responsedatabase;

FIG. 3 illustrates the treatment-response database in prospectivelyevaluating and generating treatments;

FIG. 4 depicts the relationship between therapeutic entities based onthe rules shared by their respective clusters;

FIG. 5 describes an exemplary method for identifying agents for devisinga treatment for a subject;

FIG. 6 illustrates an exemplary method for evaluating neurophysiologicinformation of subjects having a known response to an agent;

FIG. 7 illustrates another exemplary method for re-evaluatingneurophysiologic information of subjects having a known response to anagent to determine beneficial responses to the agent;

FIG. 8 illustrates an exemplary method for correlating a treatmentsignature with neurophysiologic information of a subject;

FIG. 9 illustrates an exemplary method for evaluating a subject forinclusion in a clinical trial;

FIG. 10 illustrates an exemplary method for administering a singletherapeutic entity in accordance with the invention;

FIG. 11 illustrates an exemplary method for administering multipletherapeutic entities in accordance with the invention;

FIG. 12 illustrates an exemplary method for identifying an enriched setof subjects for identifying and isolating common genetic factorsunderlying response to various conditions amenable to common treatments;

FIG. 13 illustrates a multivariable and clustering of data in itscontext;

FIG. 14 illustrates a portable device based on the small footprintenabled by the identification of rules by the system and method of theinvention; and

FIG. 15 illustrates an embodiment for remote treatment and assessment bythe methods of the invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed to a method and system for modulatinga subject's brain physiology. The invention enables integration ofneurophysiologic information and behavioral data for predicting theoutcome of treatment of a subject. In an important respect, theprediction is independent of the traditional diagnosis, and, thus is notlimited by the accuracy of the clinical diagnosis or the behavioral dataunderlying the clinical diagnosis.

The present invention is based, in part, upon the inventors' discoveriesthat quantitative neurophysiologic information, preferably includingquantitative electrophysiologic information, is a reliable indicator bywhich to choose therapies for individuals with behaviorally-diagnosedpsychiatric conditions and to predict outcomes from selected therapies.It has been discovered that such quantitative information is morereliable and useful for guiding treatment of mental disorders thantraditional diagnostic classifications arrived at by standardqualitative psychiatric procedures known in the art, which are largelybased on interview, observation, and the like. In fact, according to thepresent invention, effective therapy is administered with little if anyattention to the particular behavioral diagnosis.

The inventors believe, without limitation, that quantitativeelectrophysiologic information, such as than obtained from quantitativeelectroencephalogram recordings (QEEG), reflects more closely underlyingcentral nervous system (or, more specifically, brain) physiologicalfunctioning upon which therapies, specially therapeutic entitytherapies, directly act. Indeed, QEEG data provides regional information(anterior, central, posterior, left, and right) on CNS functioning whichreflects the well-known regionalization and lateralization of CNSfunctioning. In contrast, qualitatively reported or observed behavior isbelieved to be a net result of many factors so that any given behaviormay be the single outcome of at least several different constellationsof CNS physiological functioning, each constellation best addressed bydifferent therapies. Accordingly, it is believed that quantitativeneurophysiologic information is more reliable for selecting therapy thanis traditional behavioral diagnosis alone.

Therefore, according to the present invention, therapies forbehaviorally-diagnosed psychiatric conditions are selected according tothe indications of quantitative neurophysiologic information. Prior tothe present invention, therapies were selected primarily solely on thebasis of the behavioral diagnosis, such as a diagnosis according to astandard like the DSM-IV. It is well known, however, that therapies soselected are often ineffective, or less than sufficiently effective, ormay actually exacerbate the original complaint. Therefore, practitionersexpected significant trial and error, unpleasant side-effects, cost,patient effort, and so forth in arriving at an effective therapy. Thus,this invention provides a method and system for improving the likelihoodof selecting an effective treatment the first time, with or without apreceding traditional diagnosis of a mental disorder.

Until the present invention, quantitative neurophysiologic and QEEG datawas not thought to be useful for treatment selection because the greatcomplexity of this data effectively hid the information that the presentinvention is able to discern. Originally, EEG data was presented only asanalog waveforms, which were useful only to detect strikingabnormalities in the time domain. Thus, EEG data has long been used todiagnose prominent epilepsies. Analog data could not be used to detectsubtle changes in physiological functioning of the CNS. Althoughquantitative EEG techniques produced numerical measures of EEG activity,QEEG data also hid useful information in the many hundreds tosubstantially more than a thousand separate measures of EEG structure.These measures include principally Fourier transforms, amplitudes, andcorrelations of unipolar data, which is derived from signals recordedfrom single EEG leads, and bipolar data, which is derived fromcombinations of signals from two EEG leads.

In view of the basic discoveries underlying the present invention, theinventors have further discovered methods and systems for extractinginformation useful for therapy selection from this mass of formerlyimpenetrable quantitative neurophysiologic data. These novel methods arenow briefly and generally described in order to prepare for the specificdescriptions of particular embodiments and applications of these methodsand systems which occurs subsequently. The present description is anon-limiting summary, while the subsequent specific descriptions presentactual details of the various embodiments and applications consistentlyand completely.

Therefore, generally, the methods of the present invention begin withdata collection for a number of individuals, where for each individualthe data (collectively named, for example, a therapy-response database)includes at least an initial QEEG data, a therapy which is thenadministered, and a quantitative assessment of the response to therapy.Preferably (and not limiting), the individuals in the database have abehaviorally-diagnosed psychiatric condition; their initial QEEG istaken in a therapeutic entity-free condition; QEEG data is transformedto reflect a relative deviation from observations made in individualswithout any psychiatric symptoms; and a single therapy is thenadministered. The database, of course, can include additional data oneach individual, for example, the traditional behavioral diagnosis.

For the purposes of description only (and without limitation as toimplementation), the methods of this invention can be described andvisualized in spatial terms. Thus, the therapy-response database can berepresented as points in a space (QEEG space). QEEG space has a largenumber of dimensions, typically substantially more than one thousanddimensions, one dimension recording the values of each (normalized and“raw”) QEEG measure. Each point represents an individual in thedatabase, the point positioned according to the individual's QEEGmeasures and labeled both by the individual's therapy and whether or notthe individual was responsive to the therapy administered. Next, asdiscovered by the inventors, points (that is, individuals) that areresponsive to particular therapies tend to be arranged in “clusters,” orin “localized” groups in QEEG space. Although, these clusters or groupsmay be thought of as, for example, “galaxies” of responsive individuals,the shapes of these galaxies are not limited to compact regions, but aremost often highly, even unimaginably, complex regions in thisthousand-plus dimension space.

However complex, in an embodiment of the invention the boundaries ofthese clusters of responsive points define the QEEG measures, that isthe structures of a new patient's EEG, which predict likely response ofthat patient to the therapies defining the clusters. In other words, ifthe point representing the new patient's QEEG is in or near a clusterdefined by a particular therapy, then that therapy is selected for thenew patient according to the invention.

It is important, and one principal aspect of this invention, that thisclustering is largely independent of behavioral diagnosis. The clustersare preferably defined by being responsive only to particular therapies;other clustering conditions, such as diagnosis, are preferably not used.If, in an embodiment, diagnosis is part of the clustering, only the mostgeneral diagnostic information is useful. For example, it may be usefulto cluster separately individuals whose behaviorally-diagnosedpsychiatric condition depends on other medical conditions from those nothaving such identifiable conditions. Such conditions might includemetabolic abnormalities due to renal or hepatic disease, tumor, trauma,and the like. In contrast, the prior art teaches just the opposite,namely “clustering” individuals according to their diagnosis (that is“diagnosing” individuals) and then using such diagnostic clusters toselect therapies in a conventional manner. To the extent QEEG data hasbeen objectively used in psychiatry prior to the present invention, ithas been to diagnose, with therapy selection dependent on diagnosis. Thepresent inventors have discovered that methods opposite to the prior artare considerably more effective.

The methods of this invention now proceed by finding and representingthe boundaries of the clusters or groups of points (individuals in thedatabase) responsive to a particular therapy. In one embodiment,identification and representation of groups is performed directly in thethousand-plus dimension QEEG space. This is advantageous in thatclusters are most accurately represented without approximation in thisspace defined by the full complement of measures representing thestructure of a patient's EEG. It is less advantageous in thatrepresenting shapes and boundaries in such a high dimensional space islaborious. In this space, cluster boundaries may be represented byfunctions of the thousand-plus dimensions. For example, a cluster fortherapy T may have a boundary represented by function, f, so that for apatient point, p, if f(p)>0 then p is in the cluster. In this case, T isindicated for patient p, and not indicated for patients q with f(q)<0.Thus, f=0 may be considered as defining a “:hyper-plane” dividingpatients for which T is indicated from other patients. However, even iffor a patient q, f(q)<0, for example, therapy T may still be consideredif the point q is sufficiently “close” to the defined cluster. As mostgenerally understood, such functions, which mark out the boundaries ofclusters, define “indicative variables,” that is variables indicating,or not, particular therapies.

Therefore, in preferred embodiments, QEEG space is projected, or moregenerally, mapped (or both projected and mapped) into a “reduced” QEEGspace (simply, a reduced space) of lower dimensions in such a mannerthat clusters or groups of responsive patients are substantiallypreserved. Preferably, the reduced space has between 50 and 200dimensions, and more preferably, the reduced space has between 50 and100 dimensions, while less preferably the reduced space has more than200 hundred dimensions. The actual number of dimensions in animplementation is limited by the effectiveness of the availableclustering techniques and the computational resources for performingthis clustering. Projections are preferably defined by dropping QEEGmeasures that are determined to make little contribution to clusteringin the reduced space, where the contribution of a measure may bedetermined by analyzing the sensitivities of clusters in the reducedspace to the particular measure.

A mapping is preferably defined by combining disjoint sets of multipleQEEG measures into single variables that define the coordinates in thereduced space (for example, combining sets of 10 QEEG measures intosingle variables reduces 1000 dimensions to 100 dimensions). Preferably,the disjoint sets include QEEG measures having related physiologicalsignificance. For example, monopolar signals are combined to representthe power spectrum (divided in the standard frequency bands of alpha,beta, delta, and theta) in the standard anatomic regions (anterior,central, posterior, left, and right). Bipolar signals are combined torepresent the power spectrum of simultaneous activity between variousbrain regions, for example, across the midline. Measures in the sets aregenerally combined according to functions monotonic in all variables,such as linear combinations, non-linearly normalized linearcombinations, sigmoidal functions, or so forth.

In the following detailed descriptions, QEEG measures are often called“univariate measures,” or “univariates,” or “univariables,” or so forth.The variables defining the reduced space are called “multivariatemeasures,” or “multivariates,” or “multivariables,” or so forth. Inpreferred alternatives certain dimensions of the reduced space aredefined by single univariables, or by raw QEEG measures, such asabsolute power. Preferred actual mapping/projections are presented astables defining the multivariables into terms of the univariables.Further, actual mappings (as well as the number of reduced spacedimensions) may be iteratively improved by comparing clustering orgroups in QEEG space with the mapped clusters in the reduced QEEG space,and adjusting the mapping so that mapped clusters reproduce the originalclusters with substantial fidelity.

Thus, in preferred embodiments, cluster boundaries are determined andrepresented in a reduced QEEG space. Here, as in QEEG space, clusterboundaries may be represented by functions, or “indicative” variables,which are more manageable being functions of, preferably, 100 or fewervariables. In both spaces, clusters or groups defined by therapyresponsiveness may be determined by known clustering methods, forexample, statistical methods such as tree clustering, k-meansclustering, and the like. Alternatively, cluster boundaries (andindicative variables) may be found and represented by neural networks.Also, cluster boundaries are typically approximate, or “fuzzy.”Preferably, a boundary is chosen so that a determined percentage of theindividuals responsive to the therapy being clustered are within theboundary, while a similar determined percentage of all the individualsresponsive to the therapy are within the boundary. A practicaldetermined percentage has been found to be 80%; other percentages mayalso be used, for example, 55%, 60%, 70%, 90%, 95% or higher.

In a further preferred embodiment, a reduced QEEG space may be furthersimplified, without essential loss of clustering, into what can beconceptualized as a multi-dimensional binary cube (a “binary” reducedQEEG space), that is as the space {0, 1}^(N) (“0” and “1” may represent,for example, “true” and “false”). In a particular preferred embodimentdescribed subsequently, N=72. This binary space is realized by, forexample, dividing the range of each coordinate, or parent multivariable,defining a reduced space into two portions so that a corresponding“reduced” multivariable has the value 1 if the value of the parentmultivariable is in the first portion, and is 0 otherwise. Thus areduced space may be further mapped into a binary reduced space. Apreferred method for dividing the range multivariables is to select afirst portion with more probable values, or more normal values, and asecond portion with less probably, or more abnormal values. For example,more and less probably may be systematically chosen as 1 or 2 standarddeviations from a normal average. In this embodiment, reducedmultivariable are called “rules” in the following, and the value 1 ortrue (or 0 or false) is assigned to the less (or more) probable values.In alternate embodiments, parent multivariable ranges may be dividedinto three or more portions.

It has been found possible, through an iterative process or trial andimprovement, that the multivariable and their ranges defining a binaryreduced space may be chosen so that cluster boundaries have aparticularly compact representation, which is most convenientlyillustrated by example. Thus, consider that R_(i) (i=1, . . . , N) arereduced multivariables, or rules, defining a reduced space; and alsothat, for example (R_(I)=0) is 1, or true if R_(i) is in fact “0,” andis 0 or false if R_(i) is in fact “1” (and conversely for (R_(I)=0)).Then cluster boundaries might be represented by exemplary Booleanfunctions. For example, an exemplary Boolean function is f (R₁, R₂, R₃,. . . , R_(N))=(R_(I)=1) & (R_(J)=0) & (R_(K)=0) & (R_(L)=1), whichmight define the cluster f>0 (with f<=0 being not in the cluster).Boolean functions, which represent rule combinations are a particularlypreferred representation of an “indicative” variables. For example,general Boolean functions, perhaps expressed in conjunctive ordisjunctive normal forms, are capable of representing general decisiontrees of rules. Certain subsequently described particular embodiments,which express clusters in decision trees, may thus be alternativelyexpressed with Boolean indicative variables.

Although this invention has been described in terms of clusteringaccording to outcomes of individual therapies, considerations ofstatistical significance and computational complexity may makeclustering of lower resolution preferable. For example, a particulartherapy-response database may have an insufficient number of symptomaticindividuals to allow clusters for all individual therapies to bedetermined with reasonable significance. Certain therapies are simplyrare in or absent from the database. Alternatively, the computationalcost of finding, defining, and mapping all such clusters may be too higheven if sufficient individuals were present. In these, cases therapiesmay be grouped, and clusters of individuals responsive to any therapy ofthe group are determined. Typically, therapies group accordingphysiological similarity. For example, all therapies known to effect aparticular neurotransmitter system in a particular manner grouptogether. Thus, clustering is of varying degrees of resolution.

Now summarizing this general description, according to the presentinvention therapies are selected, and therapeutic outcomes are selected,for patients with behaviorally-diagnosed psychiatric conditions notaccording to behavioral diagnosis, but instead by comparison to adatabase of symptomatic individuals who have had positive responses tovarious therapies or classes of therapies. Therapies are then selectedfor a patient that have been successful in similar individuals.According to the invention, similarity is assessed by comparison of thepatient's quantitative neurophysiologic information with that of theindividual in the database. Preferably, the quantitativeneurophysiologic information compared includes QEEG data, and thecomparison proceeds by first clustering the quantitative informationinto clusters or groups predictive of response to the various therapiesrepresented in the database.

This clustering and comparison proceeds in the original QEEG data space.More preferably, the original QEEG space is mapped into reduced spacesthat permit simpler clustering and comparison while preserving the groupstructures present in the original data space. Such a mapping is, forinstance, made by combining the univariate measures defining theoriginal data space into multivariate variables, where each multivariatevariable is a combination (linear or non-linear) of data measuresreflecting similar CNS physiological activities. Further, a reducedspace is “discretized” by specifying ranges for the multivariatevariables that correspond, for example, to normal and abnormal (forexample, in a statistical sense) and assigning discrete values to thereduced multivariate variables, known as “rules” in this embodiment.Discretization preferably results in a space similar to ahigh-dimensional binary cube. In whatever space, the boundaries oftherapeutic clusters define characteristics of a patient's quantitativeneurophysiologic information predicting a responsive outcome to theassociated therapy. These boundaries are defined by functions, known asindicative variables. In a binary reduced space, indicative functionsare rules and Boolean combinations of rules.

This general description is not limiting at least in that these methodsare applied to arrive at results other than selection of a therapy for apatient. For example, as described subsequently, these methods are usedto select multi-therapies; or they are further be used to selectpatients likely to respond to a therapy under test. Further, a clustercontains further information. Since clustering or grouping isindependent of diagnosis, a cluster associated with a likely response toa particular therapy usually contains individuals having many diagnoses,even though they have similar quantitative neurophysiologiccharacteristics. Accordingly, the methods of the present invention leadnaturally to the use of therapies for new diagnoses, i.e., for patientswith diagnoses that heretofore were not treated with the now indicatedtherapies. The therapeutic armamentarium of the health professional isthereby broadened.

Lastly, before a more detailed description of particular embodiments andaspects of the present invention, the meaning of certain common usefulterms are explained. Typically, these meanings are clear from thecontext, and correspond to the understanding of one of ordinary skill inthe art. Use of these terms in a contrary fashion is indicated whenappropriate.

“Behaviorally diagnosed” is taken to refer to individuals who havepsychiatric complaints that are classified according to a system ofpsychiatric diagnosis, preferably according to a standard system.Preferably, the psychiatric complaints and the behavioral diagnosis areprimary, and not secondary to other medical conditions such as metabolicabnormalities or anatomic lesions. The present invention is applicableto those with other conditions. However, it is preferably to group suchpatients separately from those without other conditions.

In more detail, behavioral diagnosis is diagnosis of mental illnessbased on behavioral indicia, as observed by psychiatrists and otherhealth care professionals and codified by the DSM-UV, or its othereditions (American Psychiatric Association. Diagnostic and StatisticalManual of Mental Imbalances. DSM IV. Fourth Edition. Washington, D.C.:American Psychiatric Association), or the International Classificationof Diseases (ICD) (posted at http://cedr.lbl.gov/icd9.html, last visitedJan. 26, 2000) or similar classification systems.

“Neurophysiologic information” is the quantitative information measuredfrom the brain or from the CNS generally. It may includes quantitativemeasures of anatomic information concerning the CNS generally, such asthat obtained by magnetic resonance imaging (MRI) or computerizedtomography (CT). It also may include information measuring metabolic orother biological processes occurring in the CNS, such as that obtainedby functional MRI, positron/electron tomography (PET), or single photonemission computer tomography (SPECT). This quantitative neurophysiologicinformation is distinguished from behavioral information, relied uponfor making traditional diagnosis, obtained from interviews, observationof behavior, impressions and reports of impressions of delusion,confusion, responsiveness, dexterity and the like.

The nature of the quantitative neurophysiologic information. especiallythe conditions during its recording, has been found to be important sothat selected therapies or predicted responses will accurately reflectwhat will be observed during the routine daily functioning of patients.Simply, it is preferable that data be recorded from patients undisturbedand in a normal state of consciousness. For example, consciousnessshould not be impaired by sedative agents, hypnotic agents, anaestheticagents, or the like; also, patients should not be asleep or drowsy.Patients should be normally alert and awake during data collection.Further, since it has been found that background functioning of theentire CNS reflects treatment outcomes, patients should not be disturbedduring data collection.

Preferably, therefore, quantitative neurphysiologic information includeselectronic or magnetic impulses reflecting ongoing CNS activity in apatient in a comfortable, resting, but alert state without sensorystimuli. The eyes should be closed and the environment free fromdisturbance. Information so recorded has been found to reflect thebackground functioning useful in the present invention.

Most preferred in current embodiments is data from EEG ormagneto-encephalography experiments where the patient is resting, witheyes closed, but alert. Currently, most preferred is QEEG information,which is EEG information which have been digitized and Fouriertransformed, and, possibly, expressed as deviations from observations inpatients without psychiatric or medical conditions. Naturally,information useful in this invention typically does not includebispectral indicia, special sensory evoked potentials or nocturnalpolysomnographic data. However, this is not intended to indicate thatthe methods of the present invention are not useful in enhancing theanalysis of such information.

This quantitative neurophysiologic information is distinguished frombehavioral information, relied upon for making traditional diagnosis,obtained from interviews, observation of behavior, impressions andreports of impressions of delusion, confusion, responsiveness, dexterityand the like.

“Reference distribution” is a distribution or a set of values useful formeasuring significant deviations from normalcy as opposed to randomvariations. A reference distribution need not always be obtained fromdata taken from exclusively asymptomatic subjects. In an embodiment ofthe invention, a reference comprises data points, corresponding to“normal” or asymptomatic age-matched controls, exhibiting a Gaussiandistribution.

“Z-scores,” a type of normalization transformation, are uniformdifferential probability scores. The difference between an observedneurophysiologic value and the expected reference mean, such as“age-adjusted normal” mean divided by the expected reference standarddeviation, such as “age-adjusted normal” standard deviation yields aZ-score corresponding to the observed neurophysiologic value.

A “magnitude-outcome” (or a quantitative or objective outcome) of atreatment is a score of the relative magnitude of the change in apatient's psychiatric condition, rather than a description of itsdetails. Quantitative outcomes permit comparison of the same therapy indifferent conditions or of difference therapies for the same condition.An illustrative example is the clinical global improvement scores(“CGI”) providing a numerical score in the range [−1, 3] to indicate theeffect of a treatment. Of course, binary state changes are included insuch an outcome indicator. Moreover, magnitude outcome includes relianceon a steady state for a prescribed period of time or use of tests thatyield information that can be compared to that from prior toadministering a treatment.

A “multivariable” is a combination of univariate variables identified asbeing significant in describing or characterizing a cluster of subjects.The univariate variables are often scaled in the course of making thecombination to ensure reference to a uniform scale with requisitesensitivity. In particular multivariables define a mapping ortransformation from a typically very high dimensionality data space to amore tractable lower dimensionality space for performing the methods ofthis invention.

A “treatment” or “therapy” may include any known psychiatric therapy,including for example therapeutic entity therapy, talk therapy,convulsive therapy, photo therapy, and so forth. Preferably, the presentinvention is applied to therapies including the administration of atherapeutic entity or combination of therapeutic entities. In one sensea treatment includes a class of therapeutic entities and therapy whilein another sense it includes a specific agent.

A “paroxysmal event” is a brief sudden disturbance in the background EEGeasily visualized in the time domain. It often consists of shortduration spikes and waves, which are often but not always accompanied bya sudden voluntary or involuntary muscle movement.

A “nonparoxysmal event” is an artifact-free background EEG, theartifacts being the short duration spikes and waves indicative of aparoxysmal event.

“Approved practice” (or “approved clinical practice” or “approvedtherapeutic practice”) refers to the uses of therapies, in particular oftherapeutic entities, approved by the relevant regulatory body, which inthe United States is the Food and Drug Administration (FDA). Suchregulatory bodies typically approve therapies for use only after theirsafety has been established, and usually also only after their efficacyhas been proven in clinical trials. In the United States, approvedpractice is indicated on FDA approved labeling, which for therapeuticentities, is gathered in the Physician's Desk Reference.

Returning to the description of the invention, the invention is based,in part, upon the discovery that neurophysiologic information can andneeds to be relied upon to greater extent than the customary practice intreating patients. It is typical for a subject diagnosed in accordancewith a standard like DSM-IV to undergo a treatment only to discover thatthe treatment is ineffective. Moreover, many treatments recommended forthe same DSM-IV diagnosis may actually exacerbate the original complaintresulting in significant trial and error with its unpleasant sideeffects. In an aspect, the invention provides a method and system forimproving the likelihood of selecting an effective treatment with orwithout a preceding traditional diagnosis of a mental disorder.

More particularly, the method of the invention employs neurophysiologicinformation for assessing, classifying, analyzing and generatingtreatment recommendations for modulating brain function.Neurophysiologic information used independently of a traditionaldiagnosis enables an independent estimation of the likely response of aparticular subject to a treatment of, among other things, mentaldisorders. Notably, the invention has broad utility in providing amethod for modulating brain function in general.

Now, detailed aspects and embodiments of the present invention aredescribed. Each such embodiment or aspect is intended for separateapplication. In an embodiment of the invention, neurophysiologicinformation collected from a subject is transformed to enable itscomparison with like data from other subjects. The neurophysiologicinformation employed in the present invention is collected with the aidof instruments. Such information yields objective information in theform of EEG/QEEG signals, MRI signals, PET signals, SPECT signals, andthe like that are distinguishable from the traditional behavioralobservations of a subject to diagnose a mental disorder.

More particularly, the methods of the invention employ neurophysiologicinformation for assessing, classifying, analyzing and generatingtreatment recommendations for modulating brain function.Neurophysiologic information used independently of a traditionaldiagnosis enables an independent estimation of the likely response of aparticular subject to a treatment of, among other things, mentaldisorders. Notably, the invention has broad utility in providing amethod for modulating brain function in general.

Although the invention is described herein in its various embodimentsenabling a broad range of neurophysiologic data, most preferablyincluding EEG data, to select therapy or predict therapeutic outcomes,the present invention is to be understood to have application to diseasecategories in addition to behaviorally diagnosed psychiatric conditions.A first category includes central nervous system (CNS) conditions thatare considered on the boundary of psychiatry and neurology, beingconsidered either psychiatric or neurologic. For example, central painsyndromes are such conditions. The techniques of the present invention,in particular selecting therapy based on a comparison of a patient'sneurophysiologic data with a database of similar patients havingsuccessful outcomes to a variety of treatments, may be successfullyapplied to this category.

A second category is patients having primarily neurological disorderswith a psychiatric component. Depression secondary to loss of functiondue to stroke is such a condition. For this category it is preferably tofocus attention on a patient's, and on comparable individuals', EEGdata. Here, the techniques of the present invention are applied to EEGdata by comparing a patient's EEG data to a database of the EEG datafrom successfully treated individuals (the comparison being preferablyexpressed also as rules, as explained subsequently). Finally, thepresent invention is applicable to patients with frankly neurologicconditions. By focusing on EEG data for these patients, centrally actingtherapies are recommended to alleviate part, or a substantial part, oftheir symptoms.

Briefly, in an embodiment of the invention, neurophysiologic informationcollected from a subject is transformed to enable its comparison withlike data from other subjects. The neurophysiologic information employedin the present invention, collected with the aid of instruments, yieldsobjective information in the form of EEG/QEEG signals, MRI signals, PETsignals, SPECT signals, and the like that are distinguishable from thetraditional behavioral observations of a subject to diagnose a mentaldisorder. In an embodiment of the invention, the neurophysiologicinformation is transformed relative to a reference distribution, e.g., aZ-transform to gauge deviation from the reference distribution andpermit comparison among various measures comprising neurophysiologicinformation.

In an illustrative embodiment of the invention, EEG information iscollected from electrodes placed at standard locations on a subject'sscalp using, by convention, the International 10/20 System for electrodeplacement. The information is digitized and then undergoes fast Fouriertransform (FFT) signal processing to yield a QEEG spectrum. In additionto quantifying the power at each frequency averaged across the QEEGspectrum for each electrode, FFT signal processing of the raw EEG signalprovides measurement and quantification of other characteristics ofbrain electrical activity.

The QEEG spectrum is presently divided into four frequency bands: delta(0.5-3.49 Hz); theta (3.5-7.49 Hz); alpha (7.5-12.49 Hz); and beta(12.5-35 Hz). The spectrum also includes the results from each of theEEG electrodes represented as quantitative output measurements for eachfrequency band. These include absolute power in each band (μV²);relative power in each band (percentage power in each channel);coherence (a measure of synchronization between activity in twochannels); and symmetry (the ratio of power in each band between asymmetrical pair of electrodes). It should be noted that alternativeband descriptions, including new standards being debated, are intendedto be within the scope of the invention.

Although not intended as a limitation of the invention, the relationshipbetween these univariate measurements and brain activity is as follows.Absolute power is the average amount of power in each frequency band andin the total frequency spectrum of the artifact-free EEG informationfrom each electrode, and is believed to be a measure of the strength ofbrain electrical activity. Relative power is the percentage of the totalpower contributed for a respective electrode and a respective frequencyband, and is believed to be a measure of how brain activity isdistributed. Symmetry is the ratio of levels of activity measuredbetween corresponding regions of the two brain hemispheres or regionswithin an hemisphere in each frequency band and is believed to be ameasure of the balance of the observed brain activity. Coherence is thedegree of synchronization of electrical events in given regions of thetwo hemispheres or regions within an hemisphere and is believed to be ameasure of the coordination of the observed brain activity. Forinstance,

Using the aforementioned univariate measures, univariate Z scores, oruniform differential probability scores are calculated. UnivariateZ-scores for a quantitative output measurement are calculated, bydividing the difference between an observed value and the mean for theexpected “normal” value by the standard deviation of the expected“normal” value. The “normal” values are provided by a commerciallyavailable database such as the “Neurometric Analysis System”manufactured by N×Link, Ltd., of Richland, Wash. Information regardingthis product is presently accessible at the web-site(http://www.biof.com/nxlink.html; last visited Jan. 25, 2000). TheZ-transformation process scales all relevant information into units ofprobability (or units reflecting probability), yielding a uniform scalein all dimensions that can simplify further comparisons and evaluationsof relationships between features.

An EEG/QEEG instrument, such as the Spectrum 32, manufactured byCaldwell Laboratories, Inc. (Kennewick, Wash.), readily executes theseunivariate neurometric Z transformations. This instrument containsage-defined norms in databases of age regression expressions defining adistribution of features as functions of age in a normal/asymptomaticpopulation. The instrument extracts from the database the mean value andthe standard deviation to be expected for each feature of a group of“normal” subjects the same age as a patient. It, then, automaticallyevaluates the difference between the value of each feature observed inthe patient and the age-appropriate value predicted by the database ageregression expressions. The instrument subsequently evaluates theprobability that the observed value in the patient belongs to the“normal” group, taking into account the distribution of values in the“normal” group. A completely analogous process can be accomplished usinga family of different digital EEG machines and commercially availableneurometric software, such as that available from N×Link, Inc.

The example asymptomatic neurophysiologic information database includesthe QEEGs, i.e., neurophysiologic information, of individuals from 6 to92 years of age incorporating information from electrodes placed inaccordance with the international 10/20 System. The asymptomaticdatabase contains over 1000 quantitative univariate EEG measures. TheZ-score, obtained by comparing an individual patient's QEEG informationwith the information for the reference asymptomatic population,represents the patient's statistical deviation from thereference-asymptomatic database. Thus, if a patient's Z-score for aparticular measure does not statistically deviate from the referenceasymptomatic population, the patient would be determined to be“asymptomatic” for that measure. However, if a patient's Z-scorestatistically deviates from the reference population for a particularmeasure, the patient is determined to be symptomatic for that measure.Notably, mere examination of a Z-score reveals the extent of deviationsince a value of greater than one indicates a deviation of more than onestandard deviation from the expected mean.

A treatment-response database of symptomatic individuals is created inaccordance with the invention or a readily available treatment-responsedatabase, such as the outcome database owned by CNS Response of LongBeach, Calif. USA, accessed to generate one or more indicativevariables. Alternatively, in an exemplary embodiment of the invention,the indicative variables are provided directly to enable analysis ofunivariate data with the aid of rules. An exemplary embodiment isimplemented as a hand-held or portable device, or software for executionon computing machines such as personal organizers, personal computers orworkstations, or even software accessible over the internet. Thegeneration of the rules and the identification of indicative variables,such as multivariables, underlying the practice of the invention isdescribed next.

In an embodiment of the invention, an indicative variable is determinedfrom neurophysiologic information. A multivariable obtained by combiningvarious univariate variables describing a cluster of neurophysiologicinformation is an example of such an indicative variable. Suchmultivariables enable searching a database, for instance, foridentifying responses to a particular treatment, or a group of subjectshaving similar multivariable values (and their associated treatments)and the like. Or alternatively, testing the multivariable by applyingrules enables evaluating a treatment's outcome in a particular subject.Typically, more than one multivariable is generated and the result ofapplying various rules to the values of respective multivariables iscompared to the expected result for a particular treatment or outcome.Thus, the outcome of a particular treatment can be estimated as well aspossible treatments ranked or merely listed to provide a practitionerwith a prediction of the efficacy of various options.

Initial or pretreatment neurophysiologic information, classified asabnormal based on comparison to the neurophysiologic data from areference population, enables generation of a treatment-responsedatabase, e.g., an outcome database in an embodiment of the invention.This example outcome database contains neurophysiologic information fromsymptomatic individuals exhibiting clinical manifestations ofpsychiatric disorders and an indicator of their response to treatment asindicated by active-treatment neurophysiologic information.

A typical treatment-response database 100 illustrated in FIG. 1comprises entries containing identification information 105, casehistory of the subject including prior treatment history 110, initial orpre-treatment neurophysiologic information 115, magnitude-outcome of atleast one of the treatments 120, other measure of treatment outcome 125,active-treatment neurophysiologic information 130, membership inclusters 135, additional information such as notes on differenttherapeutic entities and their known or suspected interactions 140, andrules, indicative variables or results of applying the rules 145. Ofcourse, not every embodiment of treatment-response database 100 needhave all of the possible entries listed in a non-exhaustive manner inFIG. 1. It is expected that typically treatment-response database 100will have entries corresponding to at least twenty-five subjects,preferably entries corresponding to at least one hundred subjects andeven more preferably entries corresponding to at least three hundredsubjects. In an exemplary embodiment of the invention treatment-responsedatabase 100 is dynamic and distributed. For instance, interconnectionof several small databases on different computers, each possiblycompiled in the course of various otherwise independent studies,provides an embodiment of treatment-response database 100 taught by theinvention. Each of the entries depicted in FIG. 1 is briefly discussednext to further illustrate the nature and purpose of treatment-responsedatabase 100.

Identification information 105 includes a label or mechanism to connecttogether different information about the same subject. Exampleidentification information 105 includes name, address, social securitynumber, driver license number and the like. Prior treatment history 110preferably includes enough information to enable a determination to bemade as to whether the subject is adequately therapeutic entity-free.This is significant not only from the perspective of avoiding harmfulcross-reactions between different therapeutic entities, but also toincrease the accuracy of the evaluations made possible by the invention.For instance, the outcome database of CNS Response includes only thosesubjects who have been drug-free for at least seven half-lives ofpreviously administered therapeutic entities. Such subjects providepre-treatment neurophysiologic information as opposed to an initialneurophysiologic information. In some applications, in view of long-termeffects of some therapeutic entities, it is desirable to makepredictions of response to a treatment made with the aid ofpre-treatment neurophysiologic information. In addition, using initialneurophysiologic information in alternative embodiments of the inventionwill further take into account prior therapeutic entity history.

Initial or pre-treatment neurophysiologic information 115 discussedabove is one of the core components of the treatment-response database100. Predictions of treatment outcome are made based on matching suchinformation. Typically, EEG based neurophysiologic information includesunivariate measures of brain activity discussed previously. These may bein the form of a set of composite traces or in the form of Z-transformedvalues reflecting relative distribution with respect to a referencedistribution.

Another core component is magnitude-outcome of a treatment 120reflecting a clinical judgment of the consequences of a course oftreatment. For instance, clinical global index (CGI) assigns a score inthe interval [−1, 3] to a treatment. A value of −1 indicates worseningof the condition, 0 indicates no change, 1 indicates a minimalimprovement, 2 indicates a moderate improvement while 3 indicatesabsence of the original symptoms, a recovery, or total remission. Manyalternative schemes that represent changes in several factors into asingle or few scores can be advantageously employed to provide a commonmeasure of the efficacy of different treatments.

Active-treatment neurophysiologic information 130 is not necessarilyrequired for predicting a response to a treatment since the response toa treatment 125 is typically included as magnitude-outcome. However, itis a convenient alternative to magnitude-outcome 125 or a concurrentindicator of response to treatment. Active-treatment neurophysiologicinformation 130 provides another measure of a response to treatment, forinstance, after comparison to initial or pre-treatment neurophysiologicinformation 115. In some embodiments of the invention, active-treatmentneurophysiologic information 130 may suffice to generate a measuresimilar to magnitude-outcome 125, reflecting normalization of the EEGsignals following treatment. However, the normalization is of someselected univariate variables rather of all univariate variables.

Membership in clusters 135 is another feature of the treatment-responsedatabase 100 that is advantageously included rather than rederived eachtime treatment-response database 100 is used. In an aspect of theinvention, pre-treatment or initial neurophysiologic information 115 isclustered by various techniques so that each cluster corresponds to aselected one or set of outcomes and one or more selected treatments.Additionally, measures are taken to reduce the false negatives in eachcluster while ensuring maximal coverage of pre-treatment or initialneurophysiologic information 115 of subjects having similar outcomes oftreatments. Storing the results of a clustering analysis saves effortsince a fresh analysis is required only upon addition of significantnumber of subjects to the treatment-response database 100.

Notes on different therapeutic entities and their known or suspectedinteractions 140 is yet another useful but optional entry. Suchinformation allows the treatment recommendations generated by thetreatment-response database 100 to be checked to rule out deleteriousinteractions at the outset rather than have a physician or pharmacy flagsuch potential mishaps, or worse incur the risk of cross-reactionbetween therapeutic entities. Such information may be in a separate setof records or only of records pertinent to the treatments received or tobe received by a particular subject or group of subjects.

Finally, advantageously, in a manner similar to membership in clusters130, treatment-response database 100 includes rules, indicativevariables or results of applying the rules 145 to provide a readyreference to significant results of a cluster analysis. While notrequired for practicing the invention, such information enables rapiddatabase searches and evaluation of treatment recommendations.

FIG. 13 illustrates a cluster boundary along with a two dimensionalrepresentation of a rule. FIG. 13 also illustrates the utility of theclustering strategy in generating treatment strategies prospectively. Amultivariable is plotted against the CGI outcome for eighty-three (83)patients treated with D-amphetamine. The fifty-five (55) patients in acluster of sixty-one (61) patients, as described below, were assignedvarious DSM diagnosis including Adjustment Disorder With Anxiety;Adjustment Disorder With Disturbance of Conduct; Anorexia Nervosa;Attention-Deficit/Hyperactivity Disorder Combined Type;Attention-Deficit/Hyperactivity Disorder Predominantly Inattentive Type;Depressive Disorder NOS; Dysthymic Disorder; Major Depressive DisorderRecurrent; Major Depressive Disorder Single Episode;Obsessive-Compulsive Disorder; Oppositional Defiant Disorder; andTrichotillomania. Subsequent analysis of the EEG data revealed thatsixty-one (61) of the eighty-three (83) patients exhibited values for amultivariable that defined a cluster with a boundary at ‘0’. Of thesesixty-one (61) patients, fifty-five (55) exhibited a positive responsewhile six (6) were false positives. On the other hand there were five(5) false negatives and seven (7) of the eighty-three (83) patients werecorrectly distinguished by the multivariable as not belonging to thecluster.

FIG. 2 illustrates an illustrative exemplary method for using atreatment-response database in accordance with the invention. Duringstep 200 neurophysiologic information is collected from a data-source.The data-source could be a patient being evaluated or stored/transmitteddata. Although, such data is likely to be EEG/QEEG data due to its readyavailability in a suitable form, this is not a requirement forpracticing the invention. Next, during step 205, the neurophysiologicinformation is represented as univariate variables. As is apparent, thisis a convenient choice rather than a necessary condition since any otherrepresentation reflects merely a different choice of resolution andcoordinate transformation.

In the event a cluster is required to satisfy thresholds different fromthose either presumed or provided as default for both including truepositives and excluding false positives, such thresholds are specifiedduring step 210. A convenient threshold requires that at least eightypercent of pre-treatment neurophysiologic information of subjectssubsequently displaying a specified outcome to a treatment should beincluded in a cluster.

During step 215, one or more clusters are generated to form aggregatesof pre-treatment neurophysiologic information. In alternativeembodiments of the invention initial neurophysiologic information isclustered. The clusters are generated with an input of either aneducated guess at the number of clusters or data in the multidimensionalspace defined by the univariate variables is clustered with no such apriori assumptions.

Notably, many therapeutic entities correspond to adjacent clusterswithin a common region of the multidimensional space. Moreover,different related therapeutic entities can then be thought of asdefining a class of therapeutic entities or treatments that are suitablefor similar initial or pre-treatment neurophysiologic information.

Interestingly, many therapeutic entities that would otherwise not beconsidered to be similar, and that are typically prescribed fordifferent traditional diagnosis actually cluster together whiletherapeutic entities commonly prescribed for the same traditionaldiagnosis do not cluster together. Thus, the observed heterogeneityencountered in treating traditional diagnosis is also reflected in theclustering. Therefore, the clusters enable prediction of the response ofa subject based on whether the pre-treatment neurophysiologicinformation falls within a cluster, and thus reducing trial-and-errorstrategies presently forced upon physicians with its (now avoidable)risks. Similar results are made possible in an exemplary embodiment ofthe invention with the use of suitable initial neurophysiologicinformation.

During step 220, the boundary defining one of the clusters is examinedto identify univariate variables of interest. This process can beillustrated by analogy to the familiar three-dimensional space withembedded therein a plurality of two-dimensional planes, one dimensionallines and points lacking dimensions. For instance, in three-dimensionalspace, y=0 specifies a plane including the origin, the x-axis and thez-axis in the familiar notation. In this example ‘y’ is a variable ofinterest. Similarly, univariate variables of interest are identified. Ifthere are several univariate variables then it is convenient torepresent them in an indicative variable, e.g., a single multivariable.This is easily done with Z-transformed univariate variables by, forinstance, merely adding them together or computing a function having thedifferent univariate variables as its arguments. Some examples ofindicative variables or multivariables deduced in this manner arepresented in TABLE 1 (below) while TABLE 2 presents the correspondingcustomary electrode positions for EEG/QEEG based neurophysiologicinformation. Alternative electrode placements and modes of datacollection in other embodiments of the invention are treated in ananalogous manner. The underlying univariate variables are furthermodified in actual usage to adjust for sensitivity and ease of use asdescribed next.

For instance, if the number of univariate variables is large, it ispossible that the combined multivariable is not sensitive to changesthat include or exclude a small number of subjects from the cluster.This addresses possible concerns stemming from the intended prospectiveuse of the cluster to provide superior treatment. Moreover, the clusteris identified using retrospective data (and data as it is collected)that is susceptible to modification by addition of new data. However,alternative choices of multivariables can just as easily address aperceived need for greater certainty.

Accordingly, the multivariable combination of the univariate variablesneed not be a simple sum and instead is chosen to be a functionexhibiting the requisite sensitivity. The detailed form of the functionis advantageously determined empirically although some simple forms canbe arrived at analytically. TABLE 3 shows some useful illustrativetransformations that should not be interpreted to be a limitation on thescope of the invention.

Accordingly, during step 225 if a decision is made to transform theunivariate variables, then control flows to step 230, during which atransformation, for instance one of the transformations presented inTABLE 3, is carried out. Then control moves to step 235. Alternatively,if the indicative variable has one univariate variable then controlflows to step 235 from step 225. The multivariables are presented inTABLE 1 while TABLE 3 lists some of the functions that have beenactually used. These non-exhaustive lists are primarily illustrative ofthe invention in the context of the described embodiment.

The variables in TABLE 1 are represented by four letter abbreviations.The first two or three letters of the abbreviations are primarydesignators. The primary designators RB, RM, CA, CE, FM, AA, and AEindicate what type of QEEG measurement is referenced. For example, theprimary designator “RM” represents relative monopolar power. “RB” isrelative bipolar power. “CA” is intrahemispheric coherence. “CEB”represents interhemispheric bipolar coherence. “FM” represents monopolarfrequency. “AA” represents intrahemispheric asymmetry. And, “AE”represents interhemispheric asymmetry.

The one or last two letters of the multivariable abbreviations aresecondary designators. The secondary designators indicate the groups ofelectrodes and frequency bands from which the measurements are drawn.Measurements are drawn from electrodes in the anterior or (“A”),posterior (“P”) regions of the scalp, the left (“L) or right (“R”) sidesof the scalp. Measurements are made in the delta (“D”), theta (“T”),alpha (“A”), or beta (“B”) frequency bands.

According to TABLE 1, “RMAD” (relative power monopolar anterior delta)is the relative monopolar power in the delta frequency measured at theelectrodes located on the front half of the scalp. Similarly, “RBDL” isthe relative bipolar power measured by the electrodes in the left halfof the scalp for the delta frequency band. “CABL” is intrahemisphericcoherence measured from the electrodes in the left region of the scalpin the beta frequency band. “CADR” is the intrahemispheric coherencemeasured at the electrodes in the right region of the scalp for thedelta frequency band. “AED” is monopolar asymmetry measuredinterhemispherically in the delta frequency band.

TABLE 1 NAME DESCRIPTION NAME DESCRIPTION RMAD Relative power CABLBeta - Left Monopolar Anterior Delta RMPD Posterior Delta CABR Beta -Right RMAT Anterior Theta FMAD Frequency Monopolar Anterior Delta RMPTPosterior Theta FMPD Posterior Delta RMAA Anterior Alpha FMAT AnteriorTheta RMPA Posterior Alpha FMPT Posterior Theta RMAB Anterior Beta FMAAAnterior Alpha RMPB Posterior Beta FMPA Posterior Alpha CEAD CoherenceFMAB Anterior Beta interhemispheric Anterior Delta CEPD Posterior DeltaFMPB Posterior Beta CEAT Anterior Theta AADL Asymmetry IntrahemisphericDelta - Left CEPT Posterior Theta AADR Delta - Right CEAA Anterior AlphaAATL Theta - Left CEPA Posterior Alpha AATR Theta - Right CEAB AnteriorBeta AAAL Alpha - Left CEPB Posterior Beta AAAR Alpha - Right AEMDAsymmetry AABL Beta - Left interhemispheric Monopolar Delta AEMT ThetaAABR Beta - Right AEMA Alpha CEBD Coherence interhemispheric BipolarDelta AEMB Beta CEBT Theta AEBD Asymmetry CEBA Alpha interhemisphericBipolar Delta AEBT Theta CEBB Beta AEBA Alpha RBDL Relative powerBipolar Delta Left AEBB Beta RBDR Delta - Right CADL Coherence RBTLTheta - Left intrahemispheric Delta - Left CADR Delta - Right RBTRTheta - Right CATL Theta - Left RBAL Alpha - Left CATR Theta - RightRBAR Alpha - Right CAAL Alpha - Left RBBL Beta- Left CAAR Alpha - RightRBBR Beta - Right

TABLE 2 INDICATIVE ELECTRODES VARIABLE 1 2 3 4 5 6 7 8 9 10 11 RMAD Fp1Fpz Fp2 F3 FZ F4 F7 F8 C3 Cz C4 RMPD T3 T4 T5 T6 P3 Pz P4 O1 Oz O2 RMATFp1 Fpz Fp2 F3 FZ F4 F7 F8 C3 Cz C4 RMPT T3 T4 T5 T6 P3 Pz P4 O1 Oz O2RMAA Fp1 Fpz Fp2 F3 FZ F4 F7 F8 C3 Cz C4 RMPA T3 T4 T5 T6 P3 Pz P4 O1 OzO2 RMAB Fp1 Fpz Fp2 F3 FZ F4 F7 F8 C3 Cz C4 RMPB T3 T4 T5 T6 P3 Pz P4 O1Oz O2 CEAD FP1/ F3/ F7/ C3/ FP2 F4 F8 C4 CEPD T3/ T5/ P3/ O1/ T4 T6 P4O2 CEAT FP1/ F3/ F7/ C3/ FP2 F4 F8 C4 CEPT T3/ T5/ P3/ O1/ T4 T6 P4 O2CEAA FP1/ F3/ F7/ C3/ FP2 F4 F8 C4 CEPA T3/ T5/ P3/ O1/ T4 T6 P4 O2 CEABFP1/ F3/ F7/ C3/ FP2 F4 F8 C4 CEPB T3/ T5/ P3/ O1/ T4 T6 P4 O2 FMAD Fp1Fpz Fp2 F3 FZ F4 F7 F8 C3 Cz C4 FMPD T3 T4 T5 T6 P3 Pz P4 O1 Oz O2 FMATFp1 Fpz Fp2 F3 FZ F4 F7 F8 C3 Cz C4 FMPT T3 T4 T5 T6 P3 Pz P4 O1 Oz O2FMAA Fp1 Fpz Fp2 F3 FZ F4 F7 F8 C3 Cz C4 FMPA T3 T4 T5 T6 P3 Pz P4 O1 OzO2 FMAB Fp1 Fpz Fp2 F3 FZ F4 F7 F8 C3 Cz C4 FMPB T3 T4 T5 T6 P3 Pz P4 O1Oz O2 AEMD FP1/ F3/ F7/ C3/ T3/ T5/ P3/ O1/ FP2 F4 F8 C4 T4 T6 P4 O2AEMT FP1/ F3/ F7/ C3/ T3/ T5/ P3/ O1/ FP2 F4 F8 C4 T4 T6 P4 O2 AEMA FP1/F3/ F7/ C3/ T3/ T5/ P3/ O1/ FP2 F4 F8 C4 T4 T6 P4 O2 AEMB FP1/ F3/ F7/C3/ T3/ T5/ P3/ O1/ FP2 F4 F8 C4 T4 T6 P4 O2 AADL F3/ F7/ F3/ F7/ T5 T5O1 O1 AADR F4/T6 F8/T6 F4/O2 F8/O2 AATL F3/T5 F7/T5 F3/O1 F7/O1 AATRF4/T6 F8/T6 F4/O2 F8/O2 AAAL F3/T5 F7/T5 F3/O1 F7/O1 AAAR F4/T6 F8/T6F4/O2 F8/O2 AABL F3/T5 F7/T5 F3/O1 F7/O1 AABR F4/T6 F8/T6 F4/O2 F8/O2CADL Fp1/F3 T3/T5 C3/P3 F3/O1 CADR Fp2/F4 T4/T6 C4/P4 F4/O2 CATL Fp1/F3T3/T5 C3/P3 F3/O1 CATR Fp2/F4 T4/T6 C4/P4 F4/O2 CAAL Fp1/F3 T3/T5 C3/P3F3/O1 CAAR Fp2/F4 T4/T6 C4/P4 F4/O2 CABL Fp1/F3 T3/T5 C3/P3 F3/O1 CABRFp2/F4 T4/T6 C4/P4 F4/O2 RBDL C3/Cz T3/T5 P3/O1 F7/T3 RBDR C4/Cz T4/T6P4/O2 F8/T4 RBTL C3/Cz T3/T5 P3/O1 F7/T3 RBTR C4/Cz T4/T6 P4/O2 F8/T4RBAL C3/Cz T3/T5 P3/O1 F7/T3 RBAR C4/Cz T4/T6 P4/O2 F8/T4 RBBL C3/CzT3/T5 P3/O1 F7/T3 RBBR C4/Cz T4/T6 P4/O2 F8/T4 AEBD C3Cz/ T3T5/ P3O1/F7T3/ C4Cz T4T6 P4O2 F8T4 AEBT C3Cz/ T3T5/ P3O1/ F7T3/ C4Cz T4T6 P4O2F8T4 AEBA C3Cz/ T3T5/ P3O1/ F7T3/ C4Cz T4T6 P4O2 F8T4 AEBB C3Cz/ T3T5/P3O1/ F7T3/ C4Cz T4T6 P4O2 F8T4 CEBD C3Cz/ T3T5/ P3O1/ F7T3/ C4Cz T4T6P4O2 F8T4 CEBT C3Cz/ T3T5/ P3O1/ F7T3/ C4Cz T4T6 P4O2 F8T4 CEBA C3Cz/T3T5/ P3O1/ F7T3/ C4Cz T4T6 P4O2 F8T4 CEBB C3Cz/ T3T5/ P3O1/ F7T3/ C4CzT4T6 P4O2 F8T4

TABLE 3 Name Description Transform & Weighting Function RMAX^(a)Relative power Monopolar Anterior${12/10}{\sum\limits_{10}^{1}\; {{Electrode}_{1}\mspace{14mu} \ldots \mspace{14mu} {Electrode}_{10}}}$RMPX^(a) Relative power Monopolar Posterior${12/11}{\sum\limits_{11}^{1}\; {{Electrode}_{1}\mspace{14mu} \ldots \mspace{14mu} {Electrode}_{11}}}$FMAX^(a) Frequency Monopolar Anterior${12/10}{\sum\limits_{10}^{1}\; {{Electrode}_{1}\mspace{14mu} \ldots \mspace{14mu} {Electrode}_{10}}}$FMPX^(a) Frequency Monopolar Posterior${12/11}{\sum\limits_{11}^{1}\; {{Electrode}_{1}\mspace{14mu} \ldots \mspace{14mu} {Electrode}_{11}}}$CEAX^(a) Coherence interhemispheric Anterior$\sqrt[3.6]{\sum\limits_{4}^{1}\; {{Electrode}_{1}^{3}\mspace{14mu} \ldots \mspace{14mu} {Electrode}_{4}^{3}}}$AEMX^(a) Asymmetry interhemispheric Monopolar$\sqrt[3.6]{\sum\limits_{8}^{1}\; {{Electrode}_{1}^{3}\mspace{14mu} \ldots \mspace{14mu} {Electrode}_{8}^{3}}}$AEBX^(a) Asymmetry interhemispheric Bipolar$\sqrt[3.6]{\sum\limits_{4}^{1}\; {{Electrode}_{1}^{3}\mspace{14mu} \ldots \mspace{14mu} {Electrode}_{4}^{3}}}$AAYX^(a) Asymmetry intrahemispheric$\sqrt[3.6]{\sum\limits_{4}^{1}\; {{Electrode}_{1}^{3}\mspace{14mu} \ldots \mspace{14mu} {Electrode}_{4}^{3}}}$CEBX^(a) Coherence interhemispheric$\sqrt[3.6]{\sum\limits_{4}^{1}\; {{Electrode}_{1}^{3}\mspace{14mu} \ldots {\mspace{11mu} \;}{Electrode}_{4}^{3}}}$RBYX^(a) Relative power Bipolar$\sqrt[3.6]{\sum\limits_{4}^{1}\; {{Electrode}_{1}^{3}\mspace{14mu} \ldots \mspace{14mu} {Electrode}_{4}^{3}}}$CAYX^(a) Coherence intrahemispheric$\sqrt[3.6]{\sum\limits_{4}^{1}\; {{Electrode}_{1}^{3}\mspace{14mu} \ldots \mspace{14mu} {Electrode}_{4}^{3}}}$^(a)X = D, T, A, B; ^(a)X = D, T, A, B; Y = L, R

During step 235 the multivariable is scaled to provide a uniform scaleof reference for all multivariables. For instance, in the describedembodiment to provide a value in the interval [−40, 40] such that fourstandard deviations are spanned on each side of the mean. Alternativescaling strategies, e.g., using the interval [−10, 10] or variant numberof standard deviations are employed in alternative exemplary embodimentsof the invention. Moreover, the transformation and scaling operationscan be carried out in a single step if desired as is illustrated inTABLE 4.

TABLE 4 illustrates the transformation depicted in TABLE 3 for themultivariable CEAD (represented as entry CEAX). TABLE 4 includes boththe transformation and the subsequent scaling. The weighting functiondepicts the transformation while the rows below describe a possiblescaling operation. For instance, the components are paired by addition,squared separately and then added to get a positive whole number. Thisnumber is made negative if the sum of the terms generated by thetransformation is negative, else it is made positive. Typically, anumber between −40 and 40 is obtained with truncation of valuesexceeding these limits. Since the likelihood of multivariable CEADhaving a value outside the range is rather small the truncationoperation is rarely invoked.

TABLE 4 Component 1 Component 2 Component 3 Component 4 Electrode pairFp1/Fp2 F3/F4 F7/F8 C3/C4 Univariate Z Score −0.982 −1.036 −1.230 −0.249Weighting Function,  $\sqrt[3.6]{\sum\limits_{i = 1}^{4}\; {Electrode}_{i}^{3}}$ 0.985−1.030 −1.188 −0.314 Fp1/Fp2 + F3/F4 −2.015 F7/F8 + C3/C4 −1.502 SquareCollected Terms 4.060 2.256 Sum of Squares 6.316 Sign Correction^(a) −1CEAD 6

TABLE 5, below, illustrates an alternative scheme:

TABLE 5 Component 1 Component 2 Component 3 Component 4 Electrode pairFp1/Fp2 F3/F4 F7/F8 C3/C4 Univariate Z −0.982 −1.036 −1.230 −0.249Weighting Function, −0.947 −1.112 −1.861 −0.015 C³ Collect TermsFp1/Fp2 + F3/F4 −2.059 F7/F8 + C3/C4 −1.876 Square Collected 4.239 3.520Sum of Squares 7.760 Sign Correction^(a) −1 CEAD −8 ^(a)negative if sumof terms is negative

TABLE 5 indicates that the CEAD multivariable is calculated fromreadings collected at four electrode pairs, designated by their namesunder the International 10/20 system. The electrode pairs are referredto as components 1-4. Z scores are calculated for each electrode pair.The Z scores are transformed by a weighting function, C³, as indicatedin TABLE 3. The process of transformation makes it possible tomathematically combine the Z scores. The square is calculated for thesum of each of the components of CEAD. The values are then mapped into a“clinical decision” interval ranging from −40 to +40. This mappingcreates an integer scale of uniform change for each of the multivariabledescriptors. Thus, the weighted Z scores calculated for the electrodepairs within the same brain hemisphere were summed(Fp1/Fp2+F3/F4=−2.059; F7/F8+C3/C4=−1.876), squared, (−2.059²=4.239;−1.876²=3.520), and added together (4.239+3.520=7.760). The sign of thefinal product was corrected and rounded off to the nearest whole number(−7.760→−8).

As is readily evident, many alternative schemes, such as squaring allterms following transformation and adding them, are possible and areintended to be included within the scope of the invention.

Following scaling, control passes to step 240 although the ordering ofthe steps is clearly arbitrary and does not imply a limitation on thescope of the invention. During step 240 a rule is generated, typicallydescribing the boundary of the cluster, so that membership in a clusteris tested easily by applying a set of rules to a corresponding set ofmultivariables/indicative variables. This aspect of the inventionenables analysis without requiring a fresh clustering step or access toan overloaded database. Moreover, handheld devices, portable devices andvarious grades of software providing evaluation of therapeutic entities,treatments or design of therapeutic entity testing studies are madepossible with the identification of such rules. If there is anothercluster to process then control passes to step 220 from step 245.Otherwise, the method terminates.

Additionally, the invention enables using clusters with ‘fuzzy’boundaries. Following the generation of rules in step 240 of FIG. 2, ifa substantial fraction of the rules defining a cluster associated with atreatment are satisfied by a subject's pre-treatment neurophysiologicinformation, then it is likely that that the pre-treatmentneurophysiologic information might belong to the cluster. Thus, aprediction is possible for the effect of the treatment in accordancewith the cluster although not every rule defining the boundary of thecluster is satisfied. Some example rules are provided in TABLE 6, usingthe multivariables depicted in TABLES 1-3.

TABLE 6 Index RULE 1 EEG ABSOLUTE POWER AVERAGE = >300 microvoltssquared 2 EEG ABSOLUTE POWER AVERAGE = <300 & >40 microvolts sq. 3 EEGABSOLUTE POWER AVERAGE = <40 microvolts squared 4 FRONTAL MIDLINEPROGRESSION INDEX Fpz/Cz (Alpha Band) ≧2.5 5 FRONTAL MIDLINE PROGRESSIONINDEX Fpz/Cz (Alpha Band) ≦2.5 6 FRONTAL MIDLINE PROGRESSION INDEXFpz/Cz (Alpha Band) ≧1 7 FRONTAL MIDLINE PROGRESSION INDEX Fpz/Cz (AlphaBand) ≦1 8 RATIO OF FRONTAL/POSTERIOR ALPHA INDICES ≧4 9 RATIO OFFRONTAL/POSTERIOR ALPHA INDICES ≦4 10 AVERAGE MIDLINE (Fpzθ/Fpzβ +Fzθ/Fzβ + Czθ/Czβ)/3 THETA/BETA RATIO ≧2.5 11 AVERAGE MIDLINE(Fpzθ/Fpzβ + Fzθ/Fzβ + Czθ/Czβ)/3 THETA/BETA RATIO ≦2.5 & >1.5 12AVERAGE MIDLINE (Fpzθ/Fpzβ + Fzθ/Fzβ + Czθ/Czβ)/3 THETA/BETA RATIO ≦1.513 RMAD ≧ 10 OR RMPD ≧ 10 14 RMAD ≦ −10 OR RMPD ≦ −10 15 RMAT ≧ 10 ORRMPT ≧ 10 16 RMAT ≦ −10 OR RMPT ≦ −10 17 RMAA ≧ 10 OR RMPA ≧ 10 18 RMAA≦ −10 OR RMPA ≦ −10 19 RMAB ≧ 10 OR RMPB ≧ 10 20 RMAB ≦ −10 OR RMPB ≦−10 21 CEAD ≧ 10 OR CEPD ≧ 10 22 CEAD ≦ −10 OR CEPD ≦ −10 23 CEAT ≧ 10OR CEPT ≧ 10 24 CEAT ≦ −10 OR CEPT ≦ −10 25 CEAA ≧ 10 OR CEPA ≧ 10 26CEAA ≦ −10 OR CEPA ≦ −10 27 CEAB ≧ 10 OR CEPB ≧ 10 28 CEAB ≦ −10 OR CEPB≦ −10 29 FMAD ≧ 10 OR FMPD ≧ 10 30 FMAD ≦ −10 OR FMPD ≦ −10 31 FMAT ≧ 10OR FMPT ≧ 10 32 FMAT ≦ −10 OR FMPT ≦ −10 33 FMAA ≧ 10 OR FMPA ≧ 10 34FMAA ≦ −10 OR FMPA ≦ −10 35 FMAB ≧ 10 OR FMPB ≧ 10 36 FMAB ≦ −10 OR FMPB≦ −10 37 AADL ≧ 10, OR AADR ≧ 10 38 AADL ≦ −10, OR AADR ≦ −10 39 AATL ≧10, OR AATR ≧ 10 40 AATL ≦ −10, OR AATR ≦ −10 41 AAAL ≧ 10, OR AAAR ≧ 1042 AAAL ≦ −10, OR AAAR ≦ −10 43 AABL ≧ 10, OR AABR ≧ 10 44 AABL ≦ −10,OR AABR ≦ −10 45 AED ≦ −10, OR AED ≧ 10 46 AET ≦ −10, OR AET ≧ 10 47 AEA≦ −10, OR AEA ≧ 10 48 AEB ≦ −10, OR AEB ≧ 10 49 AEBD ≧ 10 OR AEBD ≦ −1050 AEBT ≧ 10 OR AEBT ≦ −10 51 AEBA ≧ 10 OR AEBA ≦ −10 52 AEBB ≧ 10 ORAEBB ≦ −10 53 CADL ≧ 10, OR CADL ≦ −10 54 CADR ≧ 10, OR CADR ≦ −10 55CATL ≧ 10, OR CATL ≦ −10 56 CATR ≧ 10, OR CATR ≦ −10 57 CAAL ≧ 10, ORCAAL ≦ −10 58 CAAR ≧ 10, OR CAAR ≦ −10 59 CABL ≧ 10, OR CABL ≦ −10 60CABR ≧ 10, OR CABR ≦ −10 61 CEBD ≧ 10, OR CEBD ≦ −10 62 CEBT ≧ 10, ORCEBT ≦ −10 63 CEBA ≧ 10, OR CEBA ≦ −10 64 CEBB ≧ 10, OR CEBB ≦ −10 65RBDL ≧ 10, OR RBDR ≧ 10 66 RBDL ≦ −10, OR RBDR ≦ −10 67 RBAL ≧ 10, ORRBAR ≧ 10 68 RBAL ≦ −10, OR RBAR ≦ −10 69 RBTL ≧ 10, OR RBTR ≧ 10 70RBTL ≦ −10, OR RBTR ≦ −10 71 RBBL ≧ 10, OR RBBR ≧ 10 72 RBBL ≦ −10, ORRBBR ≦ −10

The example method of the present invention augments establisheddiagnostic and treatment regimens. Therapeutic entity correlation withthe outcomes database of the present invention is a useful adjunct toclinical management that helps rule out treatments that are unlikely tobe useful. Consequently, patients are spared experimentation and therisk accompanying experimentation due to both human errors andtherapeutic entity interactions. For instance, a patient on a firsttherapeutic entity that is contra-indicated in conjunction with a secondtherapeutic entity for treating the same DSM-IV diagnosis cannot beswitched over to the second therapeutic entity. A suitable interveningtime period, typically measured in half-lives of the first therapeuticentity, is required to allow the first therapeutic entity to beeliminated from the system. However, half-life of a therapeutic entitymay depend on the age, race, prior history and the like of the subjectas well as the form in which the first therapeutic entity wasadministered. Thus, there is considerable risk of errors such as due tothe patient re-ingesting leftover drug or an error in calculating therequired intervening time period and the like.

Matching neurophysiologic information from individual subjects to theneurophysiologic data of individuals with known therapeutic entityresponse outcomes generates a probabilistic treatment recommendation.Notably, this recommendation does not depend on the details of theinitial traditional diagnosis. Indeed, a recommendation can be generatedbased on the existence of a mental disorder that has not yet beendiagnosed behaviorally.

Illustratively, when expressed in Z-scores the mean value of theneurophysiologic information approaches zero for asymptomaticindividuals. It should be noted that Z-scores approaching zero are notalways the only outcome of a successful treatment. For instance, whilethe Z-scores for a particular set of variables approach zero, theZ-scores for other variables may manifest greater deviations from thereference all the while accompanied by overall clinical improvement.Notably, current therapeutic entities need not be evaluated with an eyeon bring about a desired change in the EEG of a subject.

A method for identifying indicative variables is to identify clusters ofinitial or pre-treatment neurophysiologic information such that eachcluster, if possible, corresponds to an outcome of a treatment. Theboundaries of these clusters identify univariate variables for formingmultivariables and appropriate rules for identifying appropriateclusters. In effect, each cluster corresponds to a group of subjectssharing a common response to a treatment.

The distributions of features of two groups of subjects (where thegroups, i.e., clusters, are believed to differ in some way, e.g., tobelong to different categories) can be thought of as two clouds ofpoints in a multidimensional space in which each dimension correspondsto a feature such as a univariate variable. There may be no significantdifferences between the two groups in some dimensions (i.e., in somefeatures) but there may be significant differences in other dimensions.If these clouds of points overlap (i.e., when there is no apparentsignificant difference between the two groups with respect to somefeatures) it may be possible to define a boundary through the clouds.

In an embodiment of the invention, following a determination that asubject is likely to be afflicted with a behaviorally diagnosed braindisorder results in evaluating whether the subject also manifestsneurophysiologic deviations from a reference such as an age-adjustedreference distribution of asymptomatic individuals. CorrespondingZ-scores facilitate detection and representation of such deviations. Itshould be noted that the traditional behaviorally diagnosed braindisorder is of reduced significance in detecting abnormalneurophysiologic information.

Primarily, it is the existence of conditions leading to such a diagnosisrather than the actual diagnosis itself that conveniently triggers adetection of abnormal neurophysiologic information. Thus, the relianceon the elaborate traditional diagnostic system, such as that of DSM-UV,is greatly reduced in arriving at an effective treatment strategy.

The well-known heterogeneity of therapeutic entity response associatedwith major psychiatric illnesses supports the hypothesis that variableneurophysiology underlies what is apparently the same disorder.Moreover, apparently different disorders share one or more commonneurophysiologic determinants susceptible to a common treatment. To thisend it is useful to consider initial or pre-treatment neurophysiologicinformation to deduce the efficacy of potential treatment(s) rather thanfocus on classifying the behavioral symptoms of disease.

FIG. 3 is an illustration of a treatment-response database in use forevaluating and generating treatments. Following collection ofneurophysiologic information from a subject during step 300, it isrepresented in the form of univariate variables during step 305. Duringstep 310 a treatment-response database is searched to identify a newcluster, i.e., new group of subjects having similar neurophysiologicinformation. If during step 315, if no new group is identified thencontrol flows to step 320 with the outputting of a report listingidentified treatments, if any, during step 320. Alternatively, controlflows to step 325 from step 315. During step 325 at least one treatmentoutcome associated with the group is identified. Typically, theclustering step used to form the group includes specification of theoutcome, although this is not required for practicing the invention. Thetreatment outcome is used to rank treatments during step 330 followed bythe control flowing to step 335 for updating a report. The control thenflows back to step 310 from step 335 to identify a new group associatedwith the neurophysiologic information collected from the subject duringstep 300.

FIG. 4 illustrates the relationships between some therapeutic entities.As previously explained, advantageously the rules correspond to aboundary specifying a cluster. Thus, therapeutic entities related byvirtue of occupying the same or adjacent regions of the univariatemultidimensional space also share common boundaries although this is notan absolute requirement. Moreover, the same traditional condition isoften susceptible to various therapeutic entities that are quitedifferent in their clustering properties. The agents listed in FIG. 4are commonly relied upon to treat depression although they are in atleast three different classes of clusters.

Treatments 400, occupy a non-contiguous region of univariate space,having classes defined by regions such as Class 1 agents 405, class 2agents 410 and class 3 agents 415. Within Class 1 405 is sub-classesSSRI/SNRI 420 further comprising SNRI 425 and SSRI 430. SSRI furtherinclude the familiar therapeutic entities PROZAC 435 and EFFEXOR 440.Similarly, Class 2 410 include MAOI 445 and Class 3 includes Bupropion450.

Examining the Physicians Desk Reference, 55^(th) edition (2001),published by Medical Economics Company at Montvale, N.J., for PROZAC 435reveals that (1) it has a half-life that is as long as 16 days afterchronic administration (with as many as 7% of users being even slowermetabolizers, i.e., having even longer half-lives for the activeingredient fluoxetine hydrochloride), and (2) it is contraindicated withadministration of MAOI 445 requiring an intervening period of at least14 days after MAOI 445 therapy and five weeks following administrationof PROZAC 435. Thus, without additional information if a subjectadministered PROZAC 435 is non-responsive or has an adverse response toit, then another therapeutic entity such as an agent known to be a MAOIcannot be prescribed for a significant length of time. This requireslong-term experimentation while the invention provides a predictivestrategy for choosing an effective agent. Similarly, WELLBUTRIN, anagent in the sub-class bupropion 450 is also contraindicated with MAOI445 agents. Thus, the ability to prospectively distinguish between suchagents enables effective care and treatment with lower risks ofdeleterious effects.

Prescreening is particularly important due to the presence ofcross-reactivity, switching a subject to an alternative therapeuticentity often requires waiting for the original therapeutic entity to beeliminated from the subjects' system. This requires the subject tosuffer unnecessarily or imposes a schedule for trying varioustherapeutic entities on the patient in the order of their half-lives.Furthermore, in view of the uncertainties inherent in medicine, thelikelihood of error and serious complications also increases without thebenefit of prescreening.

FIG. 5 is an exemplary method for identifying agents to devise atreatment strategy for a subject's particular neurophysiologicinformation with the aid of a list of multivariables and theirassociated rules. Neurophysiologic information is obtained as univariatevariables during step 500. Next, a multivariable is constructed from theunivariate variables during step 505. During step 510 a rule associatedwith the multivariable is applied to the value of the multivariable andthe cumulative set of consequences of applying the rules included in aresult. If the result is sufficient to indicate a treatment during step515 then control passes to step 520. During step 520 the treatment isadded to the list. Otherwise, control passes to step 525 from step 515for testing for another multivariable. If during step 525 it isdetermined that there is another multivariable to be tested then controlpasses to step 505. Otherwise, control passes to step 530 for rankingthe identified treatments followed by terminating the method.

FIG. 6 illustrates steps in an exemplary method for utilizing thecluster analysis strategy for evaluating neurophysiologic information ofsubjects having a known response to an agent. Such data may be obtainedeither in a planned set of procedures or be collated from variousstudies for further analysis. During step 600 neurophysiologicinformation is obtained, during step 605, from subject(s) exhibiting adesirable response to a treatment. Such desirable responses includedeleterious responses or clinically significant improvements or even thefailure to exhibit a response, i.e., non-responders depending on thecontext for clustering. Clustering, during step 610, neurophysiologicinformation of subjects identified during step 605 generates clusters ofinitial or pretreatment neurophysiologic information although in someembodiments of the invention active-treatment neurophysiologicinformation may be employed as well. A cluster satisfying suitableboundary conditions is identified during step 615 such that it includesa prescribed threshold of subjects identified during step 605 while,optionally, excluding remaining subjects such that no more than aprescribed fraction of false positives is included. The boundary of thecluster is examined to identify a range of values permissible for eitherthe univariate variables or for the composite multivariate variablesduring step 620. For new subjects, the identified parameter range servesas a condition precedent for pre-screening subjects for administrationof the agent during step 625.

In addition to the preceding analysis, during step 630 the relativeproportions of subjects identified during step 605 in conjunction withthe appropriate sampling frequencies enable determining the expectedfraction of subjects relative to the population of the United States (oranother reference in alternative jurisdictions) that will exhibit thedesirable response used in step 610. Such information is useful not onlyfor marketing purpose, but also provides a measure of the significanceof the agent to a particular group of potential subjects. Suchinformation is useful in identifying whether a potential formulation isan orphan drug in accordance with statutory aims in jurisdictions suchas United States that encourage bringing such therapeutic entities tomarket.

During step 635, an optional determination of whether the subjects inthe cluster have heightened susceptibility to the treatment is madefollowed by termination of the method. Such a determination has numerousapplications from educating at risk individuals of their susceptibilityto worse than expected response to addictive and recreational drugs toplanning of public education programs by local, state and nationalgovernments and other organizations. Of course, it also provides apredictive window on the expected prevalence of a particular condition(not necessarily deleterious) in the population at large.

FIG. 7 shows the steps in another illustrative exemplary method forre-evaluating data, for instance from a study that failed to find abeneficial effect in a desired threshold fraction of patients. This is acommon occurrence with promising laboratory therapeutic entities failingto benefit enough patients resulting in difficulty in evendistinguishing between a placebo and the therapeutic entity. In anadditional feature, considerable data exists for responses to a numberof therapeutic entities but their desirable effects in the context oftreating mental state are not easily identified due to the presence of asignificant number of non-responders. However, prospectiveidentification of non-responders as taught by the invention enablesdiscovery of such new uses and safe uses of known therapeutic entities.

Briefly, to this end it enables identification of one or more conditionsprecedent for indicating the use of a candidate therapeutic entity thatotherwise has failed to demonstrate effectiveness in a trial. Thisfollows from the discovery that many therapeutic entities areheterogeneous in their effect since they are effective against more thanone diagnosed condition while not being effective on all subjectssharing a common diagnosed condition. Thus, a candidate therapeuticentity appears to be ineffective or even deleterious in some subjects ifadministered in response to a common traditional diagnosis. However,prescreening the subjects with the aid of neurophysiologic informationenables selecting subjects predisposed to respond to the therapeuticentity in a desirable manner while avoiding the confounding presence ofnon-responders or subjects susceptible to adverse responses.

Univariate variable values for neurophysiologic information from aplurality of subjects is obtained for analysis during step 700 inaccordance with the invention and, preferably with the aid ofstatistical and database tools. The neurophysiologic informationcorresponding to an outcome of interest is clustered during step 705such that a cluster corresponds to a treatment and its outcome. Theneurophysiologic information in a particular cluster is evaluated duringstep 710 to determine at least one common feature. Significantly, thisfeature is not necessarily restricted to a boundary defining set ofvalues for the univariate or multivariables. During step 715, the commonfeature is used to generate a rule for prospective evaluation of newsubjects. Finally, the expected fraction of subjects relative to thepopulation of the United States (or another jurisdiction of interest)that is capable of exhibiting the desirable response is determinedduring step 720.

Generalizing the process of multivariable generation creates a table ofsimilarly derived measures for an individual patient. An exampletherapeutic entity-response-specific characterization of braindysfunction for an individual patient is summarized according to eachmultivariable in TABLE 7.

TABLE 7 Multivariable Value Multivariable Value RMAD −35 CABL 5 RMPD −23CABR 10 RMAT −40 FMAD −34 RMPT −33 FMPD −30 RMAA 40 FMAT 3 RMPA 27 FMPT5 RMAB −30 FMAA 33 RMPB −21 FMPA 15 CEAD 4 FMAB −4 CEPD 0 FMPB 10 CEAT 5AADL 0 CEPT 5 AADR 1 CEAA −1 AATL 3 CEPA 40 AATR 3 CEAB 10 AAAL 3 CEPB20 AAAR 3 AEMD −6 AABL 0 AEMT −6 AABR 0 AEMA 9 CEBD 2 AEMB −9 CEBT 2AEBD −1 CEBA 26 AEBT −1 CEBB 3 AEBA −5 RBDL −13 AEBB −1 RBDR −10 CADL 2RBTL −18 CADR 1 RBTR −21 CATL 1 RBAL 21 CATR 1 RBAR 22 CAAL 18 RBBL −12CAAR 11 RBBR −11

In the example summarized in TABLE 7, the patient has a RMAA value of40. This value would be expected to occur in the normal population only3 times in 100,000 observations. Thus, the multivariable RMAAsignificantly deviates from its expected value. A patient with this RMAAvalue is judged as having a physiologic brain imbalance of the RMAA typeand classified accordingly.

A result of applying rules to multivariables, such as that representedin TABLE 7 is compared to the result expected for a particulartreatment. Not every treatment requires that every multivariable have aprescribed range of values. Instead, it is possible to identifymultivariables that are significant in distinguishing between variousagents and treatments. For instance, a beneficial response to PROZAC isevaluated by applying rules corresponding to index numbers 1, 2, 4, 6,8, 11, 12, 14, 16, 17, 19, 25, 27, 32, 33, 35, 41, 43, 57-60, 63-67 and71 in TABLE 6 for a total of 23 rules. These rules represent a signaturefor PROZAC. Similar signatures are determined for other treatments.Notably, not all of the rules in a signature need to be satisfiedexactly. Instead, substantial agreement with the rules is sufficient tomake a prediction and rank multiple predictions.

In addition to PROZAC, several other well-known therapeutic entitieshave suitable signatures. Example signatures are listed to provide anillustrative sample of therapeutic entities suitable for evaluation bythe method and system of the invention. CLONAZAPAM is associated withrules corresponding to index numbers 2, 3, 10, 13, 15, 18, 20, 21, 23,29, 31, 34, 36, 53-56, 61, and 62 in TABLE 6 for a total of 19 rules.DEPAKOTE is associated with rules corresponding to index numbers 2, 10,15, 16, 19, 27, 34, 36, 57-60, and 71 in TABLE 6 for a total of 15rules. EFFEXOR is associated with rules corresponding to index numbers1, 2, 4, 6, 8, 11, 14, 16-17, 19, 25, 27, 32, 34, 36, 41, 43, 57-60,63-66, 69 and 71 in TABLE 6 for a total of 27 rules. LAMICTAL isassociated with rules corresponding to index numbers 3, 12, 13, 15, 18,20-21, 24, 30, 32, 34, 36, and 53-58 in TABLE 6 for a total of 18 rules.Lithium is associated with rules corresponding to index numbers 1-2, 14,16, 18-19, 25, 27, 30, 32-33, 35 59-60 63-64, and 71 in TABLE 6 for atotal of 17 rules. PARNATE is associated with rules corresponding toindex numbers 3, 5, 7, 9-10, 13, 15, 18, 20-24, 30-32, 34, 36, 53-56,65, 67, and 69-72 in TABLE 6 for a total of 28 rules. And, TEGRETOL isassociated with rules corresponding to index numbers 1-2, 11, 14, 16-17,20, 25, 32-33, 36, 57-58, 63-64, 69 and 72 in TABLE 6 for a total of 17rules. Additional drugs and their associated signatures are attached tothis specification in APPENDIX 1.

It should be noted that the signatures described above are notlimitations on the scope of the invention, but instead illustrate theinvention for a particular choice of multivariable representation ofclusters of pretreatment neurophysiologic information. Alternativerepresentations are, therefore, intended to be within the scope of theinvention.

FIG. 8 illustrates an exemplary method based on correlating a treatmentsignature with neurophysiologic data. Following acquisition ofneurophysiologic information during step 800, a treatment is selectedfrom a list of treatments during step 805. The list of treatments may beassociated with a cluster or be generated by a clinician seeking toevaluate one or more treatment entries therein. The neurophysiologicinformation is compared to the signature of the selected treatmentduring step 810. If the correlation between the neurophysiologicinformation and the signature is less than a specified threshold, thencontrol returns to step 825 for the selection of a new treatment in thelist. The use of a threshold allows tuning the rule matching to allowfor less than perfect matches, i.e., a substantial match. Otherwise,control passes to step 820. During step 820 the selected treatment isadded to an output list. During step 825 if there are additionaltreatments in the list of treatments, then control returns to step 805.Otherwise, control passes to step 830 wherein the treatments in theoutput are ranked if a different order is required, thus completing themethod. The ranking of the treatments provides an additional flexibilityby allowing, for instance the outputs associated with each of thetreatments in the list of treatments to be reflected for the benefit ofa clinician.

FIG. 9 illustrates an exemplary embodiment of the invention forevaluating a subject for inclusion in a clinical trial. As previouslynoted, the present invention further enables a method and system forscreening individual human participants for inclusion in clinical trialsof new compounds, or for known compounds for which new uses areproposed. In clinical trials, the appropriate choice of study subjectsassures that the findings of the trial accurately represent the drugresponse of the target population. Typically, an investigator who wantsto study the efficacy of a new therapeutic entity begins by creatinginclusion and exclusion selection criteria that define the population tobe studied.

The present invention enables conducting clinical trials of newtherapeutic entities or known therapeutic entities for which new useshave been indicated using “enriched” sets of test participants. Thetherapeutic entity responsivity profiles of test participants withbehaviorally defined indicia of psychopathology and related EEG/QEEGabnormalities can be accurately gauged using EEG/QEEG throughout theclinical trial period. Changes in QEEG multivariate output measurementscan then be correlated with an outcome measure such as CGI scores totrack therapeutic entity efficacy.

In an exemplary embodiment of the invention, a candidate therapeuticentity is administered to subjects having a known initialneurophysiologic information. Following treatment with the therapeuticentity candidate active-treatment neurophysiologic information revealsthe effect of the candidate substance. This effect of the substance, forinstance, is reflected in an increase in alpha frequency range dependentparameters. The substance then is deemed suitable for testing foralleviating one or more traditionally diagnosed mental conditionsassociated with a decrease in alpha frequency range dependent parametersin EEG data. Therefore, subjects exhibiting deficit in alpha frequencyrange dependent parameters, are selected for studying the therapeuticeffect of the substance. Additional specificity is possible byevaluating the neurophysiologic information at finer resolution.

In psychiatry, the clinical characteristics that have traditionallycontributed to the definition of inclusion characteristics have beenbased on behavioral diagnosis as outlined by the DSM, ICD, both citedearlier, or similar classification systems known to the art. In themethod of the present invention, EEG/QEEG information is used inconjunction with behavioral diagnosis, as an inclusion criterion toguide sample selection.

First, behavioral diagnosis typically screens potential sample subjects.However, the method and system of the present invention do not requirethe behavioral diagnosis. Second, a desired profile for studyparticipants based at least in part on EEG/QEEG abnormality patterns andoptionally the behavioral diagnosis correlates is chosen. And third,potential study participants with the desired EEG/QEEG abnormalitypatterns and behavioral correlates are recruited as potentialparticipants in the trial.

Turning to FIG. 9, the neurophysiologic information of the subject isobtained during step 900. In view of the possibility that there may bemore than one set of rules, i.e., signatures corresponding to atreatment, a signature is selected from a list of such signatures duringstep 905. For instance, there may be non-contiguous clusters associatedwith the treatment or multiple clusters associated with differentoutputs following the treatment, each having its own signature. Next,analogously with steps 810 and 815 of FIG. 8, during steps 910 and 905 adetermination is made of the correlation between the neurophysiologicinformation and the selected signature. If the correlation is less thana threshold then control passes to step 920 to evaluate anotherneurophysiologic signature, which is selected during step 925 withcontrol returning to step 910. Otherwise control passes to step 930 fromstep 915.

During step 930 the outcome associated with the treatment signature isevaluated so determine whether it is a desirable (or undesirable) forthe purpose of the proposed trial. If the associated outcome precludesincluding the subject in the trial then control passes to step 940.Otherwise, control passes to step 935 during which the subject is addedto the clinical trial and control passes to step 940. A determinationthat there is another prospective subject during step 940 results in thecontrol returning to step 900 via the step 945 for obtainingneurophysiologic information from a new subject. Otherwise the methodterminates.

As explained previously, the invention further enables better treatment,by prospectively evaluating putative treatments for diagnosed mentaldisorders. Some such disorders include, without being limited to therecited list, the following: agitation, attention deficit hyperactivitydisorder, atypical asthma, Alzheimer's disease/dementia, anxiety, panic,and phobic disorders, bipolar disorders, borderline personalitydisorder, behavior control problems, body dysmorphic disorder, atypicalcardiac arrthymias including variants of sinus tachycardia, intermittentsinus tachycardia, sinus bradycardia and sinus arrthymia, cognitiveproblems, atypical dermatitis, depression, dissociative disorders,eating disorders such as bulimia, anorexia and atypical eatingdisorders, appetite disturbances and weight problems, edema, fatigue,atypical headache disorders, atypical hypertensive disorders, hiccups,impulse-control problems, irritability, atypical irritable boweldisorder, mood problems, movement problems, obsessive-compulsivedisorder, pain disorders, personality disorders, posttraumatic stressdisorder, schizophrenia and other psychotic disorders, seasonalaffective disorder, sexual disorders, sleep disorders including sleepapnea and snoring disorders, stuttering, substance abuse, ticdisorders/Tourette's Syndrome, traumatic brain injury, trichotillomania,or violent/self-destructive behaviors.

In this aspect of the invention, the invention guides choices fortreating the above-listed psychiatric, medical, cardiac andneuroendocrine disorders with various therapeutic regimes, including,but not limited to: therapeutic entity therapy, phototherapy (lighttherapy), electroconvulsive therapy, electromagnetic therapy,neuromodulation therapy, verbal therapy, and other forms of therapy.

In an aspect of the invention, following a traditional diagnosis of asubject it is possible to further evaluate the traditional treatments todetermine the set of treatments likely to be effective in view of theneurophysiologic information obtained from the subject. This approachnot only speedily delivers care, but, also, diminishes the subject'srisk of deleterious effects from avoidable experimentation.

As an added benefit, the invention not only enables reevaluation oftraditional treatments, but also suggests non-traditional (novel orcounter intuitive) treatments that are suitable for the particularsubject's neurophysiologic information. The invention enables differentneurophysiologicly referenced treatment strategies that are safe andeffective for subjects who share a common diagnosis, because eachtreatment strategy is tailored to specific neurophysiologic information.

Conversely, many subjects having different behavioral diagnosis respondwell to the same treatment. Such subjects are treated accordingly by themethods taught by the present invention while traditional diagnostic andtreatment methods are biased by the proportion of patients that respondwell to a common set of treatments resulting in less than effectivetreatment of smaller sub-groups of patients.

In one aspect of the invention, a subject's univariate Z-scores arecompared directly with the information contained in a treatment-responsedatabase. In the therapeutic entity therapy aspect of the presentinvention, this comparison identifies a cluster, in turn defined bymultivariables, to which the subject's univariate Z-scores are related.It is possible to identify treatments that are likely to correctEEG/QEEG abnormalities by either tracking the effect of a treatment onthe subject's Z-scores directly or a sub-set of the subject's Z-scores.For example, the sub-set is conveniently chosen to include theunivariate variables included in the definitions of the multivariablesdefining the cluster. Thus, the effect of treatment on the EEG/QEEGbased neurophysiologic information allows both follow-up evaluations andanother measure of the outcome of the treatment. A clinician can usethis measure to guide additional therapeutic choices.

At least two types of analysis are possible according to the method ofthe present invention—Type-one and Type-two Analysis. Type-one Analysisprovides that subjects are therapeutic entity free. Type-two Analysis,discussed below, provides for patients who will not or cannot betherapeutic entity free. Therapeutic entity status preferably duplicatesthat of the reference distribution for calculating Z-scores. Subjectsincluded in the outcomes database are preferably free of therapeuticentity for at least seven half-lives of their prior therapeutic entityand its metabolites.

In the Type-one analysis, a subject's baseline EEG/QEEG is then matchedwith similar EEG/QEEGs and their correlated therapeutic entity outcomesin the outcomes database. As indicated, the outcomes database includestreatment modalities that convert the abnormal multivariate parametersof these patients toward normal. Next, a neuroactive therapeutic entitycandidate is identified in the outcomes database according to itsphysiological effects on brain function as indicated in the CGI scoreor—a more direct measure of the effect of a treatment on theneurophysiologic information. Since the clusters in the OutcomesDatabase are associated with a treatment and its outcome, eachtherapeutic entity is classified by its influence on EEG/QEEGinformation. This procedure furnishes the physician with a physiologicallink between the therapeutic possibilities and their effect on brainfunction across diverse symptomatic behavioral expressions.

The probability that a patient will respond to different types oftreatments is then determined. These treatments include medication,classes of therapeutic entities, psychotherapy or combination thereofincluding various known and suspected antidepressants, anti-anxietyagents, side effect control agents, treatments for alcohol abuse, moodstabilizers, anti-ADD agents, anti-psychotics, impulse control agents,antihypertensive agents, antiarrthymics, and hypnotic agents.

In addition, in an aspect of the invention it is possible to classifytreatments based on the clusters of pre-treatment neurophysiologicinformation known to be responsive in leading to a desired outcome.Presently, we term such a classification scheme based on a response to atreatment rather than a diagnosis an electrotherapeutic classification.As may be expected, such a scheme tracks the effect of the treatment onfeatures of neurophysiologic information.

For instance, in the case of EEG containing neurophysiologic informationtherapeutic entities are known that are associated with outcomes such asan alpha deficit, an alpha excess, beta excess, delta excess, thetaexcess, excess energy or abnormal coherence and combinations thereof. Inparticular it is useful to consider the following non-exhaustive list ofelectrotherapeutic classes described in terms of the outcome:

Class 1: Excessive energy in the alpha band of EEG results in an alphaexcess over the level associated with the age referenced distribution.This increase in energy is evaluated either at a single electrode or twoor more electrodes. Some exemplary indicative variables reflecting alphaenergy excess are the previously described multivariables RMAA or RMPAwith values over 10 (rule 17 of TABLE 6). therapeutic entities fallingin this class include PROZAC™ and EFFEXOR™.

Class 2 Excess energy in the theta or delta bands. This is indicated bythe value of example multivariables RMAT, RMAD, RMPD and RMPT ofTABLE 1. Example therapeutic entities include monoamine oxidaseinhibitors (MAOI) and stimulants such as Adderall. Notably,administration of MAOI's increases the energy in the alpha band.

Class 3: Energy in the alpha and theta band increases. This is indicatedby the value of example multivariables RMAT, RMAA, RMPT, and RMPA ofTABLE 1. Example therapeutic entities include WELLBUTRIN™.

Class 4: Energy in the beta band increases. This is indicated by thevalue of example multivariables RMAB and RMPB of TABLE 1. Exampletherapeutic entities include cardiovascular system affecting agents suchas beta-blockers.

Class 5: Coherence measures in EEG are affected. This is indicated bythe value of example multivariables CEAD and CEPB of TABLE 1. Exampletherapeutic entities include Lithium and Lamictal.

As is apparent, additional or alternative classifications are possiblewith no loss of generality. The aforementioned classes are useful inmaking therapeutic recommendations, particularly in a rule baseddecision-making environment where decisions reflect generalizationsgleaned from a treatment-response database rather than actual search ofthe database itself. Moreover, the use of multiple agents for treating agiven subject also benefits from the availability of classes of agentsto provide a broad choice of agents to accommodate therapeutic entitycombinations that are contraindicated or undesirable because of adverseeffects or other reasons.

The outcomes database of an embodiment of the present invention includesentries corresponding to almost three thousand patients and twelvethousand treatment episodes. It tracks treatment-response data based onEEG/QEEG information for a number of therapeutic entities known by theirgeneric names. Examples of such therapeutic entities include:alprazolam, amantadine, amitriptyline, atenolol, bethanechol, bupropionregular and sustained release tablets, buspirone, carbamazepine,chlorpromazine, chlordiazepoxide, citalopram, clomipramine, clonidine,clonazepam, clozapine, cyproheptadine, deprenyl, desipramine,dextroamphetamine regular tablets and spansules, diazepam, disulfiram,d/l amphetamine, divalproex, doxepin, ethchlorvynol, fluoxetine,fluvoxamine, felbamate, fluphenazine, gabapentin, haloperidol,imipramine, isocarboxazid, lamotrigine, levothyroxine, liothyronine,lithium carbonate, lithium citrate, lorazepam, loxapine, maprotiline,meprobamate, mesoridazine, methamphetamine, methylphenidate regular andsustained release tablets, midazolam, meprobamate, metoprolol regularand sustained release form, mirtazepine, molindone, moclobemide,naltrexone, nefazodone, nicotine, nortriptyline, olanzapine, oxazepam,paroxetine, pemoline, perphenazine, phenelzine, pimozide, pindolol,prazepam, propranolol regular and sustained release tablets,protriptyline, quetiapine, reboxetine, risperidone, selegiline,sertraline, sertindole, trifluoperazine, trimipramine, temazepam,thioridazine, topiramate, tranylcypromine, trazodone, triazolam,trihexyphenidyl, trimipramine, valproic acid or venlafaxine.

Treatment-response data based on EEG/QEEG information is also possiblefor medicinal agents having the following example trademarks: Adapin,Altruline, Antabuse, Anafranil, Aropax, Aroxat, Artane, Ativan, Aurorix,Aventyl, BuSpar, Catapres, Celexa, Centrax, Cibalith-S, Cipramil,Clozaril, Cylert, Cytomel, Decadron, Depakene, Depakote, Deprax,Desoxyn, Desyrel, Dexedrine tablets, Dexedrine Spansules, Dextrostat,Dobupal, Dormicum, Dutonin, Edronax, Elavil, Effexor tablets, Effexor XRcapsules, Eskalith, Eufor, Fevarin, Felbatol, Haldol, Helix, Inderal,Klonopin, Lamictal, Librium, Lithonate, Lithotabs, Loxitane, Ludiomil,Lustral, Luvox, Manerix, Marplan, Miltown, Moban, Nalorex, Nardil,Nefadar, Neurontin, Norpramin, Nortrilen, Orap, Pamelor, Parnate, Paxil,Periactin, Placidyl, Prisdal, Prolixin, Prozac, Psiquial, Ravotril,Remeron, ReVia, Risperdal, Ritalin regular tablets, Ritalin SR tablets,Saroten, Sarotex, Serax, Sercerin, Serlect, Seroquel, Seropram, Seroxat,Serzone, Symmetrel, Stelazine, Surmontil, Synthroid, Tegretol, Tenormin,Thorazine, Tofranil, Tolrest, Topamax, Toprol XR, Tranxene, Trilafon,Typtanol, Tryptizol, Urecholine, Valium, Verotina, Vestal, Vivactil,Wellbutrin SR tablets, Wellbutrin regular tablets, Xanax, Zoloft, orZyprexa. The generic descriptions of these trademarked agents and theirsource are available from the Physicians Desk Reference (New York:Medical Economics Company, 2001), the descriptions of which are hereinincorporated by reference.

The EEG/QEEG information of the present invention links therapeuticentities to their effects on brain function. TABLE 6 contains selectedagents in the database of the present invention, electrotherapeuticallyclassified by 72 discriminating features. A response prediction can bemade based on the magnitude of observed EEG/QEEG parameters and thesubset of rules listed in TABLE 6 that are associated with a particulartherapy.

Individuals who cannot be tested due to difficulty in obtainingneurophysiologic information in a therapeutic entity-free state aretested under conditions where ongoing therapeutic entities are allowed.This Type-two analysis reports the impact of therapeutic entity on theEEG/QEEG information. Follow-up EEG recordings are used to track changesproduced by the administration of therapeutic entities.

Of course, when Type-Two analysis has been preceded by Type-OneAnalysis, it is possible to observe the absolute changes attributable totherapeutic entity and appreciate the spectrum of actions on theEEG/QEEG of a given combination of therapeutic entities. These effectscan be compared to the set of initially comparable individuals and theirresponse to the same therapeutic entity or therapeutic entities.

For patients analyzed according to Type-two Analysis without a precedingType-one Analysis, therapeutic guidance is derived from treating theinformation as if it were derived from Type-one Analysis and adjustingtherapeutic entity using both the electrotherapeutic agentrecommendation and the current therapeutic entity information. Thisapproach takes into account the possible known complications fromtherapeutic entity interactions while treating independent therapeuticentity actions as independent. In the absence of interfering therapeuticentity interactions, this approach yields a good estimate of the actionof a drug and at least a starting point for further analysis.

Moreover, it is possible to define treatment to include a staggeredadministration of more than one substance, thus allowing the clusteringprocedure described previously to predict the response of a subject,including responses based on initial neurophysiologic informationcollected during the course of treatment for deducing treatment optionswith the aid of treatment-response database built in accordance withType-one analysis.

FIG. 10 summarizes a typical embodiment of the process of singletherapeutic entity therapy based on the preferred EEG/QEEG method of thepresent invention. During step 1000 of a therapy process, one or moreclinicians establish baseline parameters to measure various physiologicand behavioral changes. Next, during step 1005, the therapeutic entityof choice is administered to the patient in a dose based on EEG/QEEGanalysis in accordance with the invention. The choice of therapeuticentity is guided by the outcome predicted by the method and system ofthe invention for interpreting pre-treatment or initial neurophysiologicinformation. Moreover, response to the treatment is monitored, at leastin part, by examining the effect on the neurophysiologic information.While not a requirement for practicing the invention, theactive-treatment neurophysiologic information often reflects changes inindicative variables reducing deviation from age-matched referencedistributions. Accordingly, dosage is changed as needed and indicated byrepeat QEEG analysis and CGI scores during step 1010.

During step 1015 a determination is made as to whether the condition isa chronic condition. If the condition is chronic then control flows tostep 1020. Upon reaching a steady state, as adjudged by EEG-basedoutcome measures and/or other outcome measures such as CGI scores, thesteady state is maintained for chronic conditions. In the case ofnon-chronic conditions characterized by episodes of limited duration,control flows to step 1025 from step 1015. During step 1025, preferably,EEG-based outcome measures enable reduction of the dosage during step1025.

FIG. 11 summarizes an exemplary embodiment of the process of multi-agenttherapeutic entity therapy based on the preferred EEG/QEEG method of thepresent invention. It should be noted as a preliminary matter that it ispossible to suitably define a treatment as including more than oneagent. However, in view of scarce data it is useful to also retain thecapability of deducing a course of treatment from the treatment-responsedatabase having primarily single treatment outcomes on subjectsqualifying for Type-one analysis. This strategy reduces possible errorsdue to unexpected therapeutic entity interactions while retaining theability to analyze situations where different treatments do notinterfere or actually supplement each other. During step 1100neurophysiologic information for a subject is obtained. Theneurophysiologic information so obtained is either initialneurophysiologic information or pre-treatment neurophysiologicinformation. Additional neurophysiologic information is collected, whendesired, to monitor the effect of an agent following administration anddeduce the need for additional agents to effect a desired improvement.

Relying upon the neurophysiologic information, at least in part,treatment options are generated in accordance with the invention duringstep 1105. Multiple treatment options are generated if the initialneurophysiologic information belongs to, i.e., satisfies rules for morethan one cluster. During step 1110 a determination is made if there aremultiple treatments. If there is only one or no treatment generated thencontrol flows to step 1115. During step 1115 the indicated treatment, ifany is administered. The administration of the treatment preferablyfollows steps 1010-1020 of FIG. 10 of adjusting doses as needed. Thesesteps are advantageously carried out with the aid of a portable devicesuch as a suitably programmed personal assistant or even a dedicatedportable device for applying the rules deduced from cluster analysis ofthe data in the treatment-response database. However, this is not arequirement for practicing the invention. Thus, for instance, aphysician may prefer a CGI scale or an alternative measure ofimprovement or change instead. Following, suitable adjustment of doses,the method terminates.

If there are multiple treatments then control passes to step 1120.During step 1120 one of the treatments is selected based on the strengthof the match between the initial neurophysiologic information and therules/membership of the cluster corresponding to a desired outcome andthe selected treatment.

Steps 1125, 1130 and 1140 correspond to steps 1015, 1025 and 1020respectively of FIG. 10 for adjusting the dose of the treatment.Following such adjustment control flows from either step 1130 or step1140 to step 1135. During step 1135, follow-up neurophysiologicinformation is obtained either from the preceding dose adjustment stepsor a new set of data is obtained. This neurophysiologic information istreated as initial neurophysiologic information and the control returnsto step 1105 for reevaluation of this initial neurophysiologicinformation. In some instances, there is no further need for additionaltreatments and the method rapidly converges. Otherwise, additionaltreatments are generated that can supplement or even replace the firstselected treatment. Moreover, a treatment can be encountered more thanonce during execution of the iterative steps of FIG. 11.

In an embodiment of the invention, during step 1120 of FIG. 11 treatmentselection includes considering known therapeutic entity interactions. Inaddition, scheduling considerations have been developed for bettertreatment outcomes. To this end it is advantageous when faced withmultiple treatment options to administer Class 4 agents before agents inother classes. Of course it should be understood that an agent having anoutcome in more than one class can be used to simultaneously treatmultiple features if possible. In contrast to Class 4 agents, Class 2agents are administered last. Faced with a choice between Class 1 andClass 5 agents, it is preferable to administer Class 1 agents first.However, given a choice between Class 1 agents and neuroleptictherapeutic entity, the neuroleptic therapeutic entity is administeredfirst.

FIG. 14 illustrates exemplary portable devices enabled by the presentinvention, in particular with the aid of the small footprint of therules deduced from the treatment-response database. In addition, compactversions of the treatment-response database and remote diagnosis andtreatment with the aid of a communication link to a central facility arealso enabled and improved by the present invention. Laptop computer 1400and a handheld device PDA 1405 include modules for receiving input,providing output, accessing rules, making correspondences, and referencedistributions for evaluating information. In addition, subsets orcompact versions of truly extensive treatment-response databases arepossible as well.

Laptop computer 1400 and the PDA 1405 can communicate with a centralfacility 1410 via a communication link that is implemented as awireless, infra-red, optical or electrical connection including hybridcombination thereof. The central facility provides extensive analyticaltools, software, expansive databases to analyze and evaluate one or moreneurophysiologic information sets of interest. In particular, with datacollected using techniques other than EEG, data analysis is likely to bemore demanding of computational resources even with the dramaticallyimproved computational devices available today. Moreover, copyrights andintellectual rights prevent full copies of such software to be loaded onPDA 1405 and laptop computer 1400 in an economical fashion resulting ina preference for remote analysis of such data if required. Thus, theability to formulate rules to replace databases not only provides a fastand small footprint embodiment of the invention it enables manyvariations on suitable software to provide additional choices to users.Moreover, licensed users, in an exemplary embodiment of the invention,subscribe to obtain updates on rules as they are refined with the aid ofadditional data continually being added to the treatment-responsedatabase.

FIG. 15 illustrates an exemplary embodiment of the present inventionwhere patient data gathering and/or treatment may be remote from patientdata processing performed according to the methods of this invention,and where both data gathering and processing may be remote from orrequired patient evaluation or assessment. Illustrated here isdata-gathering site 1505 at which quantitative neurological information,specifically EEG information, is being obtained from patient 1501 bymeans of processing device 1503. As described above, device 1503 may bea basic EEG device for recording raw EEG data; or may be a QEEG devicecapable of certain preprocessing (for example, into z-scores) of raw,recorded data followed by remote data transmission of the raw andpreprocessed results; or may be a computer (such as a PC-type computer)in combination with an interface for receiving neurological data, suchas EEG data, that records, optionally preprocesses, and transmitsrecorded neurological data, or the like. In particular, site 1505 may bea doctor's office where data gathering is supervised by patient 1501'sphysician (who need not be psychiatrically trained), or may be in aclinical laboratory setting supervised by a technician, or may even bethe patient's home or bedside, or elsewhere

Although device 1503 is generally colocated with patient 1501 at site1505, these are in general remotely located from assessment processingcenter 1513 where gathered data is processed according to any of themethods of this invention. Accordingly, data recorded from patient 1501(along with other patient data such as demographic data, medical andtreatment history, prior test results, and the like) is transmitted toprocessing center 1513. Most simply, gathered data may be recorded oncomputer-readable medium 1507 which is then physically carried or mailedto center 1513. However, this data is preferably communicated 1509 byknown real-time communication means, such as by a LAN, or by theInternet, or by a communication link such as a leased or dial-uptelephone connection, a satellite link, or the like. Assessment results,treatment recommendations, and other output of the methods of thisinvention may then be transmitted 1511 back to the physician ortechnician at site 1505 by any of these transmitting means.

In this embodiment, patient data is processed for treatment orassessment purposes at site 1513, which includes at least computer 1515and database device 1517. Computer 1515 may for example be a workstationor server computer, and database device 1517 may be known mass storagehardware, such as one or more hard disks. Device 1517 may store programsconstructed using known software technologies and which when executed bycomputer 1515 cause it to perform the methods of this invention. Thesestored programs may also be stored on computer-readable media (ortransmitted over a network) for distribution to other assessment sites.Device 1517 may also store a treatment-response database and any otherdata used by the invention's methods for assessing patient neurologicaldata.

Patient data processing may be supervised and quality reviewed by,preferably, a psychiatrically-trained physician(s) who is present eitherat site 1513 (not illustrated) or at remote site 1519. Preferably, sucha reviewer(s) ensures that the received patient data is of sufficientquality, that the various processing steps performed at site 1513produce clinically-reasonable results from the received data, and thatany final assessment or treatment recommendations to be transmitted areappropriate in view of all the patient data. An access system (or morethan one) at site 1519 makes such information available to the revieweras is needed for the review, and may optionally permit the reviewer toadjust or control patient data processing.

Also illustrated is site 1521 where a further user (using a furtheraccess system) evaluates patient available information. Such a furtheruser may be a consulting physician who, along with a primary physician,also needs to evaluate patient data and assessments. Also, such afurther user may be gathering additional treatment-response data to addto the system database. Generally, this further user may access systemdata for reasons appropriate in the other methods of the presentinvention, such as for evaluating trials of a therapeutic agent (eithera new agent or a new use for a known agent), or for evaluating patientsfor incorporation into a planned trial of a therapeutic agent, or soforth.

It should be understood, that any two of more of the sites at whichvarious aspects of the methods of the present invention are carried out,such as illustrated sites 1505, 1513, 1519, and 1521, may be “remotelylocated” from each other, where “remotely located” refers to sites thatmay be separately located in a single city, or that may be separatelylocated in a single country or on a single continent, or that may beseparately located in different countries or on different continents, orthat may be separately located with other geographic separations.Alternatively, any two or more of these sites may be “colocated,” where“colocated” refers to sites in the same room or building, or generallywithin the extent of a single local area network (such as anintra-hospital Ethernet), or so forth. In all cases, data transmissionare preferably carried out with the security necessary or required inview of the transmission modality to protect patient confidentiality.

It should be further understood that the present invention includes boththe methods and systems directly or indirectly illustrated in FIG. 15.Such methods would generally include transmitting, processing, andreceiving occurring at remotely located or colocated sites. Such systemswould include transmitting devices, receiving devices, and processingdevices for carrying out these methods. Also the invention generallyincludes program products comprising computer-readable media withencoded programs for carrying out any or all of the methods of thepresent invention.

In another aspect of the invention, FIG. 12 illustrates the utility ofthe invention in identifying inherited traits for the subsequentidentification and isolation of genes responsible for pathways thatunderlie shared predicted responses to a treatment even when accompaniedby a spectrum of disparate behavioral symptoms. Briefly, FIG. 12represents the relationship, in a family tree, between four subjects whohad similar initial or pre-treatment EEG as measured by univariatevariables. Patient 1 1200 a 49 year old, married, right handed Caucasianwoman reported symptom set #1. Symptom set #1 comprised a first episodeof mood lability, anxiety, futility, concentration difficulties,lethargy, irritability, over-reactivity and insomnia that had beenpresent for several months. There was no suicidal ideation ordrug/alcohol use. Mental status examination revealed a pleasant femalewhose appearance, behavior and cognitive performance were within normallimits. Patient 1 1200 met criteria for Mood Disorder NOS (296.90) inaccordance with DSM.

Patient 2 1205, daughter of patient 1 1200, reported symptom set #2.Symptom set #2 comprised a recurrent episode of dysphoric mood,headaches, diffidence, incontinent crying spells, anergy and hypersomniaaccompanying three years of academic failure. There was no drug oralcohol use and no previous therapeutic entity. Mental statusexamination revealed a somber, self-disparaging teen whose cognitivetesting demonstrated inattentive mistakes on serial seven subtractionsand inability to repeat more than 4 digits backward. Patient 2 1205 metcriteria for Dysthymic Disorder, early onset (300.40), ProvisionalAttention Deficit Disorder (314.00), Provisional Learning Disorder NOS(315.9) in accordance with DSM.

Patient 3 1210, son of patient 1 1200, reported symptom set #3. Symptomset #3 comprised recurrent episodes of increasing anxiety andinvoluntary, reclusive behavior. Despite chronic academic difficulties,he had graduated from high school. He reported deficiencies in energy,mood, sociability, appetite and reading comprehension. No drug oralcohol use, impulsivity, sleep disturbance or distemper was reported.Mental status exam revealed a frustrated, amiable male who waspreoccupied with self-criticism. Cognitive examination showed inabilityto perform serial subtraction of 7's from 100 and slowness withdyscalculia during serial subtraction of 3's from 30. Digit retentionwas 5 forward and backward. Diagnoses were Anxiety Disorder withobsessive and phobic symptoms due to a learning disability (293.89),Attention Deficit Disorder (314.00), Learning Disorder NOS (315.9) inaccordance with DSM.

Patient 4 1215, mother of patient 1 1200, reported symptom set #4.Symptom set #4 comprised chronic insomnia, ascribed to an inability inquieting her mind. This complaint had proven refractory to multiplehypnotics and only slightly responsive to lorazepam. She admittedoccasional frustration and distemper, but denied any dysphoria or moodswings. Family members reported chronic mood excursions with agitation.Mental status exam revealed an engaging and optimistic woman. Cognitiveexamination was within normal limits. Dyssomnia Disorder NOS (307.47)was diagnosed in accordance with DSM [familial data suggesting AtypicalBipolar Disorder (296.8)].

Despite the different behavioral diagnosis in accordance with thecriteria set forth by DSM, patient 1 1200, patient 2 1205, patient 31210, and patient 4 1215 shared similar EEG patterns and respondedpositively to the same agents that included carbamazepine and buprupion.In contrast, two family members—sister of patient 1 1200 and a granddaughter of patient 1 did not exhibit the alpha frequency deficits. Thesister was diagnosed with dysthymic disorder, early onset (300.40). Thegranddaughter was diagnosed with an EEG that was within normal variationwith an attention deficit disorder 314.0 about 1.5 years later. Her EEGwas slightly slow for age, and the QEEG exhibited diffuse theta excess.She was successfully treated with amino acids: L-tyrosine, L-glutamineand L-glutamine and did well.

Thus, the three generations depicted in FIG. 12 share a common responseto common treatments indicating an inherited trait represented by one ormore genes. However, the individual subjects present differentbehavioral symptoms resulting in multiple diagnosis. These heterogeneoussymptoms reflect the interaction of a shared set of genes with amultitude of other genes. Therefore, isolation of a population thatshares a common set of genes of therapeutic significance is not possiblein general by methods based on DSM based diagnostic methods. On theother hand, the outcomes database in this illustration of the system andmethod of the invention readily identified an enriched set of subjectsfor further screening to isolate responsible genes and develop betteragents to modulate their action. Thus, the invention provides a methodand system to identify an enriched population of subjects that can befurther dissected to isolate finer common responses to treatment tovarious agents for isolation of genetic traits of interest.

This exemplary application of the present invention is better understoodby analogy. For instance, many agents target multiple receptors andother proteins. Anti-inflammatory agents such as aspirin, ibuprofin andthe like present such an example. These agents target both COX-1 andCOX-2 receptors. However, for pain management without side effects suchas ulceration of the stomach, it is desirable to target only the COX-2receptors. Newer therapeutic entities such as VIOXX provide suchspecificity. Similarly, to develop targeted agents for treating mentaldiseases it is necessary to have methods and system for tracking indetail the response to therapeutic entity based on the effect on mentaldisease or function. This is enabled by the treatment-response databaseas employed by the present invention since it not only predicts theresponse to treatment, but tracks a therapeutic entity by the responsethereto including possible side effects. Furthermore, it enables a finestructure analysis by identifying clusters sharing a particularresponse, such as lack of an undesirable side effect while maintaining apositive response otherwise. Such fine structure analysis requires thelarge number of subjects included in the treatment-response database ofthe invention along with the facility to repeatedly perform clusteranalysis to better define different populations of interest efficiently.

The present invention is further described in the following examplesthat are intended for illustration purposes only, since numerousmodifications and variations will be apparent to those skilled in theart. The first example describes the use of the utility of the inventionin guiding treatment following a traditional diagnosis in accordancewith a standard like the DSM. The second example illustrates theidentification of features associated with successful and unsuccessfuloutcomes of a treatment. The third example illustrates the large numberof novel uses for known therapeutic entities identified by the methodand system of the present invention.

Example patients with chronic Major Depressive Disorder (MDD),non-responsive to at least two previous therapeutic regimens of adequatedosage(s) and duration were studied. Their lack of response to repeatedprevious clinical efforts provided a clear baseline from which to noteany increase in treatment efficacy with EEG/QEEG information. Thesepatients were assigned to control (D) and experimental (D+E) treatmentgroups. Every other patient meeting the study criteria was treatedsolely on the joint decision of the treating psychiatric resident and asupervising faculty psychopharmacologist. The other group of patientswas treated using EEG directed therapeutic recommendations by the sameclinicians. Patients were evaluated to exclude concurrent illness andmedication status. After these assessments, a clinician that was not andwould not be involved in the treatment of the patient evaluated thepatient providing a basis for future assessment of treatment response bythis clinician. This evaluating physician played no role in therapeuticentity selection, had no other contact with the patient until assessingoutcome of treatment, had no knowledge of which experimental group thepatient belonged, nor any information on the EEG/QEEG findings. Thisclinician made all clinical ratings used in the analyses.

Each patient had a conventional twenty-one electrode digital EEG. Arule-based classifier analyzed normalized artifact-free epochs ofconventional EEG. A specific therapeutic entity outcome prediction,containing the correlated therapeutic entity responses ofantidepressant, anticonvulsant and stimulant classes was reported to thetreating physicians of the D+E group. Therapeutic entity outcomepredictions patients in the D group were sealed until the end of thestudy. After six weeks on a therapeutic entity(s) at maximal tolerateddosage, the independent evaluating physician using the CGI rating scaleassessed treatment efficacy.

Study outcome was also evaluated using the Hamilton Depression RatingScale [HDRS] as well as the Beck Depression Inventory [BDI]. The meanHDRS for the D group pretreatment was 24 and the active-treatment was18. The BDI for the D group pretreatment was 22 and the active-treatmentwas 20. The mean HDRS for the D+E group pretreatment was 23 and theactive-treatment was 9. The BDI for the D+E group pretreatment was 26and the active-treatment was 13. These changes in test scores betweenthe two treatment groups are highly significant (Friedman ANOVA0.2(N=13; df=3) p<0.009).

In the D+E group 6 of 7 patients had a CGI change of 2 or more;additionally 4 of 7 of these patients achieved a CGI of 3 indicating noevidence of illness. In the D group 1 of 6 patients had a CGI change of2 or more and 5 of 6 patients had a CGI change of 0 indicating noimprovement (p=0.02; Fisher's exact).

When the positive and the negative predictions are combined, twelve outof thirteen predictions were correct (p=0.015; Fisher's exact). Thiscorresponds to an 86 percent likelihood of positive patient outcome witheach prediction and Youden Index of 0.8 (Youden W J. Index for ratingdiagnostic test. Cancer 1950; 3: 32-35).

Example patients with chronic Major Depressive Disorder (MDD),determined by two senior faculty members, who had been non-responsive toat least two previous therapeutic entity regimens of adequate dosage(s)and duration were accepted in the study from consecutive evaluations ofoutpatients at the Veterans Administration Medical Center, Sepulveda.Their lack of response to repeated previous clinical efforts provided aclear baseline from which to note any increase in treatment efficacywith EEG/QEEG information. Human Subjects Committee approval of theprotocol was obtained. Informed consent was obtained from all studyparticipants.

These patients were assigned to control and experimental treatmentgroups. Every other patient meeting the study criteria was treatedsolely on the joint decision of the treating psychiatric resident and asupervising faculty psychopharmacologist. No concurrent report of thesechoices was given to the staff of this study nor did the staff of thisstudy have any part in the selection of these patients' therapeuticentity. This group was called DSM DIRECTED.

A psychiatric resident and their supervising facultypsychopharmacologist, who had agreed to follow therapeutic entityrecommendations based on EEG/QEEG correlation, treated patients notassigned to the DSM DIRECTED group. This group was called DSM+EEGDIRECTED.

Patients taking therapeutic entities other than anti-hypertensive orhormone replacement agents were disqualified because the control groupswere selected using these criteria. Also excluded were subjects with apresent or past primary psychotic diagnosis, history of intramuscularneuroleptic therapy, documented closed head injury with loss ofconsciousness, history of craniotomy, history of cerebrovascularaccident, current diagnosis of seizure imbalance, current diagnosis ofdementia, presence of mental retardation or active substance abuse.

All patients were required to be therapeutic entity-free (at least sevenhalf-lives of the longest lived therapeutic entity) and illicitsubstance free (ascertained by a urine screen for drugs on the day ofthe EEG).

Before acceptance into the study, patients were evaluated to excludeconcurrent illness. The evaluation included a physical examination withlaboratory studies consisting of a hemogram, chemistry panel, thyroidstimulating hormone, urine drug screen, β-HCG (in females) and an EKG.The treating physician then interviewed patients. Hamilton-D (HAM-D) andBeck Depression (BECK) Scale scores were obtained during this interview.

After these assessments, a clinician that was not and would not beinvolved in the treatment of the patient evaluated the patient. Thisinitial process provided a basis for future assessment of treatmentresponse by this clinician. This evaluating physician played no role intherapeutic entity selection, had no other contact with the patientuntil assessing outcome of treatment, had no knowledge of whichexperimental group the patient belonged, nor any information on theEEG/QEEG findings. All clinical ratings present were made by thisclinician.

The DSM DIRECTED group (N=6) had 4 males and 2 females, with an averageage of 45. Similarly the DSM+EEG DIRECTED group (N=7) had 5 males and 2females and an average age of 41. All patients were in similar types andfrequency of psychotherapy that was maintained for the duration of thestudy. TABLE 8 summarizes the composition of the patient population.

TABLE 8 DSM DIRECTED Number of Patients Mean/24 h in mg Fluoxetine 2 40Nefazodone 1 300 Sertraline 2 175 Clonezapam 1 2 Lithium 2 1050Valproate 2 1125 Average Number of 1.8 Medications/Patient

TABLE 9 DSM + EEG DIRECTED Number of Patients Mean/24 h in mg Valproate2 1000 Lithium 2 750 Paroxetine 1 30 Fluoxetine 2 35 Methylphenidate 227.5 Carbamazepine 2 850 Sertraline 1 100 Average Number of 1.7Medications/Patient

Each patient had a conventional digital EEG recorded from twenty-oneelectrodes were applied according to the International 10/20 System.Then, 10 to 20 minutes of eyes-closed, awake, resting EEG was recordedon a Spectrum 32 (Cadwell Laboratories, Kennewick, Wash.), referenced tolinked ears. The conventional EEG was reviewed to exclude paroxysmalevents, spikes, sharp waves, focal disturbances and other abnormalitiesapparent by visual inspection. Artifact-free epochs of conventional EEG,selected by a technician, were based on the rule that all artifact-freesegments were to be included in the sample until at least 32 epochs of2.5 seconds were obtained. EEG recordings were rejected a priori asunsuitable for further analysis due to unfavorable signal to noise ratio[less than or equal to 3:1] or if average frontal power was less than 9μV₂.

A rule-based classifier using the current patient's neurophysiologicinformation profile as described above and the database from theinventor's patient population was used to review pretreatment EEG/QEEGinformation from each study patient. An EEG/QEEG specific therapeuticentity outcome prediction, containing the correlated therapeutic entityresponses of antidepressant, anticonvulsant and stimulant classes wasreported to the patient control officer. This information wasdistributed only to the treating physician of the individual DSM+EEGDIRECTED patient, as described above. Therapeutic entity outcomepredictions for all other patients were sealed until the end of thestudy.

The treating physician and their faculty supervisor for bothexperimental groups monitored treatment in weekly follow-up sessions.The mean follow-up for the study groups was 25 weeks. After six weeks ontherapeutic entity(s) at maximal tolerated dosage, treatment efficacywas assessed by the independent evaluating physician, blind to patientstatus [DSM DIRECTED or DSM+EEG DIRECTED] and therapeutic entityregimen, who had assessed the patient prior to treatment. Thisphysician's prior knowledge of the patient permitted the use of ClinicalGlobal Improvement (CGI) ratings.

Two patients, one each in the DSM DIRECTED and DSM+EEG DIRECTED groups,had EEG records that exhibited an average frontal power of less than 9μV². Thus, no EEG/QEEG therapeutic entity prediction was made for thesepatients.

The remaining eleven patients were classified into EEG/QEEG sets basedon objective spectral features. EEG/QEEG sets included relative thetafrequency excess, i.e., predicted to be responsive to treatment withclass 2 agents. Theta excess refers to the percentage of total powercontributed by the theta frequency band in excess of that expected fromthe age-matched reference population previously noted. Similarly,relative alpha frequency excess predicted response to treatment withclass 1 agents; and interhemispheric hypercoherence and hypocoherencepredicted response to treatment with class 5 agents.

Next the outcome of the study was evaluated to determine significantdifferences or lack thereof between DSM directed treatment and DSM+EEGdirected treatment. The HAM-D for the DSM DIRECTED group showed a meanpretreatment score of 24 compared to a mean treatment score of 18. TheBECK Scale showed a mean pretreatment score of 22 compared to a meantreatment score of 20. The HAM-D for the DSM+EEG DIRECTED group showed amean pretreatment score of 23 compared to a mean treatment score of 9.The BECK Scale showed a mean pretreatment score of 26 compared to a meantreatment score of 13. These changes in test scores between the twotreatment groups are highly significant (Friedman ANOVA χ2(N=13; df=3)p<0.009).

In the DSM+EEG DIRECTED group 6 of 7 patients had a CGI change of 2 ormore; additionally 4 of 7 of these patients achieved a CGI of 3indicating no evidence of illness. In the DSM DIRECTED group 1 of 6patients had a CGI change of 2 or more and 5 of 6 patients had a CGIchange of 0 indicating no improvement (p=0.02; Fisher's exact).

All but one patient (low power) in the DSM DIRECTED group hadtherapeutic entity outcome predicted from pretreatment EEG/QEEGinformation, but this information was not reported to the treatingphysicians. When the study finished, the prediction was examined withrespect to the patient's clinical response.

DSM+EEG DIRECTED patients were treated with the agents that werepredicted by EEG/QEEG information to produce a favorable clinicaloutcome. Six of seven patients in this group responded as predicted apriori by EEG/QEEG information. When the positive and the negative apriori predictions are combined, ten out of eleven predictions werecorrect (p=0.015; Fisher's exact). This corresponds to an 86 percentlikelihood of positive patient outcome with each prediction and YoudenIndex of 0.8 (Youden W J. Index for rating diagnostic test. Cancer 1950;3: 32-35).

Therefore, patients treated in the DSM DIRECTED group had an inferiorresponse to pharmacotherapy. Only one of six patients demonstratedimproved behavioral and clinical outcome measurements by HAM-D, BECK andCGI ratings. In comparison, six of seven patients in the DSM+EEGDIRECTED group responded with significantly improved HAM-D, BECK and CGIratings. Furthermore, remission of symptoms or a CGI rating of 3 wasachieved by four of seven patients in the DSM+EEG DIRECTED group. Thesetherapeutic improvements would be unanticipated given the chronic andrefractory nature of the imbalance in this select population

This study further shows that therapeutic entity response in apparentlyrefractory patients can be predicted by EEG/QEEG information. Alsodemonstrated is the ability of psychiatric physicians to incorporateEEG/QEEG information with therapeutic entity correlation as a laboratorytest in clinical practice resulting in improved patient outcomes.

In another example embodiment of the method and system of the inventionone hundred and three (101) consecutive patients with Mood Disturbanceand Attentional Disorder were enrolled in a study. Retrospectiveanalyses identified those neurophysiologic features associated withoutcomes of pharmacotherapy.

The attentional deficit population was initially treated with a Class 2therapeutic entity, principally methylphenidate at a dose not exceeding1.0 mg/kg body weight per day. If the patient did not achieve a ClinicalGlobal Improvement score of 2 (moderate global improvement) or 3 (markedglobal improvement) after one month of therapeutic entity, the stimulantwas discontinued and secondary treatment with a Class 1 therapeuticentity was initiated. If the patient did not achieve a Clinical GlobalImprovement score of 2 or 3 after six weeks of therapeutic entity, theClass 1 therapeutic entity was augmented with tertiary treatmentconsisting of a Class 5 therapeutic entity (carbamazepine, valproicacid) or a Class 2 therapeutic entity.

Affectively disordered patients without a history of mania wereinitially treated with a Class 1 agent (heterocyclic antidepressant (upto 3.0 mg/kg/day) or a serotonin re-uptake inhibitor). If by six weeksthe patient did not achieve a CGI score of 2 or 3, then a secondarytreatment with a Class 5 agent was administered. Failure to improveafter three weeks at therapeutic plasma levels caused tertiary measuresto be instituted, most frequently a challenge with a class 2 agent. Ifthe challenge demonstrated responsivity a therapeutic trial was added tothe patient's regimen.

The population was heuristically divided into four groups based onobjective spectral features. These groups included those who exhibited,respectively, relative alpha frequency excess, relative theta frequencyexcess, inter-hemispheric hypercoherence, or patients whoseneurophysiologic information did not demonstrate one of the precedingprofiles. The four groups were identified within both attentionallydisordered and affectively disordered patients. The strikingelectrophysiologic similarity of the under and over eighteen year oldaffectively disordered groups demonstrated a robustness of thesefindings across ages.

As the findings demonstrate [TABLE 10 and TABLE 11], the patient samplesin each of the DSM diagnostic categories studied were not homogeneous inmedication response. These sub-groups were distinguishable byneurophysiologic information within each DSM category; moreover, thesubgroups were qualitatively similar across the DSM diagnosticcategories. The relative frequency of the subgroups differed between thecategories examined as well as between age groups within the affectivelydisordered population. Retrospective analyses of clinical outcomesdemonstrate differential responsivity to selected classes ofpharmacologic agents. The outcomes show that subgroups with similarneurophysiologic features responded to the same class of pharmacologicalagent despite the impact of the clinical treatment paradigm and the DSMclassification of the patient's presenting problems. That is, thepresence of the excess frontal alpha pattern was associated withresponsivity to Class 1 agents (antidepressants) whether it appeared ina patient with DSM behavioral features consistent with depressivedisorder or in a patient with DSM behavioral features consistent withattentional disorder. In this study, it was also found that patientswith hypercoherence responded to Class 5 agents(anticonvulsants/lithium) without regard to DSM diagnosis. Thesefindings demonstrate the clinical utility of the present invention. Therecognition of a physiologic feature common to treatment resistantschizophrenic, affective, and attentional disordered patients, willreduce morbidity with the practice of the invention in a clinicalsetting.

In another example embodiment of the method and system of the inventionpatients with DSM-III-R diagnoses of 296.xx, 311.00, and 314.xx wereprospectively enrolled in a study from consecutive evaluations.Retrospective analyses of the relationships between clinicalresponsivity and neurophysiologic features were performed in this studyin order to identify those neurophysiologic features associated withunsuccessful and successful outcomes of pharmacotherapy.

Two samples of therapeutic entity-free (no medicine for seven half-livesof the longest half-life agent) patients: those with affective imbalancediagnoses (296.xx or 311.00) and those with attentional imbalancediagnoses (314.xx) were identified by historic and clinical examination.These diagnoses were then confirmed in review by a second experiencedclinician. One hundred and three (103) consecutive individuals wereincluded in the study from those patients who were consideredappropriate for the testing procedure. Two patients were excluded fromthe study due to unavailability of laboratory results (Chem. 24, CBC,TSH, UDS, and HCG) or the absence of a follow-up for at least six monthsafter the initiation of pharmacotherapy.

The attentional disordered sample consisted of 46 patients, 34 males and12 females, with a mean age of 12.4 years. The affectively disorderedpopulation consisted of 54 patients, 20 males and 34 females, with amean age 13.5 years in the adolescent population and a mean age of 40.4years in the adult population.

Fifty percent of the attentionally disordered population was previouslydiagnosed and classified as treatment refractory by the referringclinician. In the affective disordered population there was a four-foldexcess of unipolar patients by DSM-III-R criteria Only one adolescentreceived the diagnosis of Bipolar Imbalance.

Treatment was monitored in weekly, bimonthly, or monthly follow-upsessions using Clinical Global Improvement (CGI) ratings. CGI's takenfrom the patient's baseline presentation were generated usinginformation gathered from parent and teacher Conner's scales, patientand parent interviews, contact with teachers, and the treatingclinician's assessment for the attentionally disordered population.

The attentional deficit population was initially treated with a Class 2therapeutic entity, principally methylphenidate at a dose not exceeding1.0 mg/kg body weight per day. If the patient did not achieve a ClinicalGlobal Improvement score of 2 (moderate global improvement) or 3 (markedglobal improvement) after one month of therapeutic entity, the stimulantwas discontinued and secondary treatment with an class 1 therapeuticentity was initiated. If the patient did not achieve a Clinical GlobalImprovement score of 2 or 3 after six weeks of therapeutic entity, theClass 1 therapeutic entity was augmented with tertiary treatmentconsisting of a Class 5 therapeutic entity (carbamazepine, valproicacid) or a Class 2 therapeutic entity.

Affectively disordered patients without a history of mania wereinitially treated with a Class 1 agent (heterocyclic antidepressant (upto 3.0 mg/kg/day) or a serotonin re-uptake inhibitor). If by six weeksthe patient did not achieve a CGI score of 2 or 3, then a secondarytreatment with a Class 5 agent was administered. Failure to improveafter three weeks at therapeutic plasma levels caused tertiary measuresto be instituted, most frequently a challenge with a class 2 agent. Ifthe challenge demonstrated responsivity a therapeutic trial was added tothe patient's regimen.

The population was heuristically divided into four groups based onobjective spectral features. These groups included those who exhibited,respectively, relative alpha frequency excess, relative theta frequencyexcess, inter-hemispheric hypercoherence, or patients whoseneurophysiologic information did not demonstrate one of the precedingprofiles. The four groups were identified within both attentionallydisordered and affectively disordered patients. The strikingelectrophysiologic similarity of the under and over eighteen year oldaffectively disordered groups demonstrated a robustness of thesefindings across ages. It was further noted that all these groups sharethe feature of delta frequency relative power deficit and twenty-fivepercent (25%) of the attentional disordered patients demonstratedinter-hemispheric hypercoherence primarily in the frontal region.

The theta excess subgroup of affectively disordered patientsdemonstrated a spectrum with global delta frequency deficit, a thetamaxima of +2.2 mean-units in the frontal polar region, a second thetamaxima of +2.4 mean-units in the posterior frontal region, and adecrease of relative theta power posteriorly. The alpha excess subgroupof affectively disordered patients demonstrated a spectrum with globaldelta frequency deficit, alpha maxima of +2.2 mean-units in the frontalpolar region, a broad frontal alpha plateau of approximately +2.0mean-units, and a second smaller alpha relative power plateauposteriorly of +1.0 mean-unit. Inter-hemispheric hypercoherence was seenin thirty-six percent (36%) of the affectively disordered adolescent andfifty-seven percent (57%) of the adult groups, mainly between thefrontal regions.

The relative frequency of each of these electrophysiologic subgroupsdiffers across these DSM-III-R diagnostic categories and by age (TABLE10) in statistically significant manner.

TABLE 10 DSM-III-R FRONTAL FRONTAL Diagnostic ALPHA THETA CategoriesEXCESS OTHER EXCESS Attentionally 25 [54%] 7 [15%] 14 [31%]  DisorderedAffectively 18 [72%] 4 [16%] 3 [12%] Disordered under 18 Years OldAffectively 17 [58%] 8 [29%] 4 [13%] Disordered 18 Years and Older

At six months after the initiation of treatment CGI ratings for thefrontal alpha and theta excess subgroups were divided into treatmentresponsive and treatment refractory patients.

Clinical response was analyzed as a function of neurophysiologicspectral findings and class(es) of pharmacotherapeutic agent(s) for thenormocoherent groups as shown in TABLE 11. The frontal alphaexcess/normocoherent subgroup was 87% or more responsive to class 1agents without regard to the patient's clinical presentation withattentional or affective symptoms. The frontal thetaexcess/normocoherent subgroup appeared only in the attentionallydisordered clinical population. In that population it was 100%responsive to class 2 agents.

TABLE 11 FRONTAL THETA FRONTAL ALPHA EXCESS EXCESS RESPONSIVE RESPONSIVETO TO ANTIDEPRESSANTS STIMULANTS AFFECTIVELY  9/10 [90%] 0 [0%]DISORDERED ATTENTIONALLY 13/15 [87%] 7/7 [100%] DISORDERED

Clinical response as a function of neurophysiologic spectral findingsand class(es) of pharmacotherapeutic agent(s) for the hypercoherentpopulations is shown in TABLE 12. Here, the frontal alphaexcess/hypercoherent subgroup was 85% or more responsive to Class 5agents without regard to the patient's clinical presentation withattentional or affective symptoms. The frontal thetaexcess/hypercoherent subgroup represented only a total of 5 patients, 4of whom (80%) were responsive to Class 5 agents.

TABLE 12 FRONTAL ALPHA FRONTAL THETA EXCESS RESPONSIVE EXCESS RESPONSIVETO CLASS 5 AGENTS TO CLASS 5 AGENTS AFFECTIVELY 17/20 [85%] 2/2 [100%]DISORDERED ATTENTIONALLY   5/5 [100%] 2/3 [67%]  DISORDERED

As the findings demonstrate, the patient samples in each of theDSM-III-R diagnostic categories studied were not homogeneous. Thesesub-groups were distinguishable by neurophysiologic information withineach DSM-II category; moreover, the subgroups were qualitatively similaracross the DSM-III-R diagnostic categories. The relative frequency ofthe subgroups differed between the categories examined as well asbetween age groups within the affectively disordered population.

Retrospective analyses of clinical outcomes demonstrate differentialresponsivity to selected classes of pharmacologic agents. The outcomesshow that subgroups with similar neurophysiologic features responded tothe same class of psychopharmacological agent despite the impact of theclinical treatment paradigm and the DSM-III-R classification of thepatient's presenting problems. That is, the presence of the excessfrontal alpha pattern was associated with responsivity to Class 1 agents(antidepressants) whether it appeared in a patient with DSM-III-Rbehavioral features consistent with depressive imbalances or in apatient with DSM-III-R behavioral features consistent with attentionalimbalances.

In this study, it was also found that patients with hypercoherentNeurometric patterns responded to Class 5 agents(anticonvulsants/lithium) without regard to DSM-III-R diagnosis. Thesefindings demonstrate the clinical utility of the present invention. Therecognition of a physiologic feature common to treatment resistantschizophrenic, affective, and attentional disordered patients, willreduce morbidity with the practice of the invention in a clinicalsetting.

The theta excess population could be divided into two subtypes: afrontal theta excess group and a global theta excess group. The frontaltheta excess group responded to Class 2 agents while the global thetaexcess group responded to Class 5 agents. The findings are consistentwith the known heterogeneity underlying DSM-III-R diagnostic categoriesthat requires significant experimentation with therapeutic entities toidentify an effective therapeutic entity.

In an embodiment of the invention, various DSM categories, for instanceorganized by chapters of DSM, are matched with agents found to beeffective by the method and system taught by the present invention. Sucha comparison is presented in TABLE 13 with known and accepted treatmentscorresponding to entries marked “C” and new or novel therapeuticentities found to be effective in a suitable sub-groups of subjectsmarked with “N.” As is apparent at a glance there are many novel usespossible for known therapeutic entities that are unknown due to the lackof a systematic method and system for discovering them. The presentinvention provides such a method and system.

The present invention has important applications beyond relatingparticular patients and particular therapies. In applications focused ontherapies, this invention provides, inter alia, a wealth of new uses forknown therapies, uses for new therapies (in particular therapies not yetapplied to behaviorally-diagnosed condition even though already used forother medical conditions), as well as new methods of determiningindications for therapies.

Therapy applications, beginning with new uses for known therapies, aredescribed with primary reference to the introductory general summary ofthe present invention. Because the clusters or groups of symptomaticindividuals described previously are selected based on responsiveness toa particular therapy and without regard to an individual's behavioraldiagnosis, each cluster or group will usually contain individuals with awide range of diagnoses. Further, because a particular therapy isrecommended for a patient when that patient's quantifiedneurophysiologic data is in or near the cluster or group, determined ina neurophysiologic data space, of individuals responsive to thattherapy, typically therapies will be selected as efficacious forpatients with diagnoses that are not yet part of locally approvedclinical practice involving that therapy. In fact, such an outcome ismost probable because the clinical trials used to establish efficacyhave heretofore usually been carried out without observation andanalysis of trial participants' quantified neurophysiologic informationaccording to the present invention. In this manner, new efficacious usesof known therapies, in particular of known therapeutic entity aredetermined.

In addition, even if a therapeutic entity, or other therapy, is not yetpresent in a particular treatment-response database, previouslydescribed embodiments of the present invention may be applied toselected patients and diagnoses that will likely be responsive to thistherapeutic entity. For example, a responsivity profile may bedetermined for the not-yet-present therapeutic entity (foreign to thedatabase) and compared to responsivity profiles of therapeutic entitiesalready present in the database (native to the database). The foreigntherapeutic entity will likely be efficacious in the same situations,i.e., for the same patients and the same diagnoses, as is the nativetherapeutic entity. If no native therapeutic entity has a responsivityprofile similar to the foreign therapeutic entity, the present inventionmay still indicate patients and diagnoses for which the foreigntherapeutic entity is likely to be efficacious in the same manner asdescribed in the previous particular embodiments which select patientsfor clinical trials. That is patients, along with their diagnoses, areindicated if their quantified neurophysiologic is close to beingcomplementary to significant aspects of foreign therapeutic entitiesresponsivity profile.

Further, therapeutic entities may be evaluated which are nottraditionally considered for psychiatric therapies. For example, cardiactherapeutic entities which affect the electrophysiologic functioning ofthe heart are determined to be efficacious for patients with particularneurophysiologic or electrophysiologic profiles.

Determination of clusters or groups and similarity of quantifiedneurophysiologic information (including, preferably, QEEG data)preferably, is in a reduced space. In particular preferred embodiments,similarity and clustering are defined in a reduced binary space of QEEGdata by rules involving multivariables and Boolean combinations of suchrules. Fuzzy, or approximate, similarity or clustering is similarlydefined by “fuzzy” Boolean functions. For example, a disjunction is truein a “fuzzy” sense if most of its terms are true (for example more than50%, or 75%, or the like, are true). In this embodiment, individual andgroup diagnostic indications are expressed compactly as rules dependingon quantitative EEG data, or other quantitative neurophysiologic data.

Moreover, this invention includes not only these described methods fordetermining new indications for therapeutic entities, but also includesthe actual therapeutic uses of these therapeutic entities in indicatedpatients or in patients with the indicated diagnoses. In certainembodiments, indications for a therapy may include simply the presenceof a behavioral diagnosis not heretofore associated or approved with theuse of the particular therapy. In other embodiments, the indications mayinclude quantified neurophysiologic criteria in place of or togetherdiagnostic information, such as a diagnostic class or a particulardiagnosis. Preferably, these indications depend on QEEG data, and mostpreferably are expressed in a reduced QEEG space, such as by rules in abinary reduced space.

TABLE 13 presents a non-exhaustive list of indications for therapeuticentities or for classes of therapeutic entities in particularbehaviorally-diagnosed psychiatric conditions, or in classes or suchconditions. Some indications (appropriately set out) are alreadybelieved to be known as part of approved clinical practice or underdevelopment for future approval. Further indications are (alsoappropriately set out) believed to be not currently known. Certainindications are believed not only not to be known, but also to besurprising in view of current scientific understanding. It is to beunderstood that the present invention covers individually all novel usesindicated in TABLE 13, whether or not novelty is correctly set out inthis table. Thus, each entry in TABLE 13 not currently part of approvedclinical practice (for example, as presented in the Physician's DeskReference) is individually covered, and covered as part of a group, withsuch provisos as necessary to exclude uses which are not novel. Theindications in TABLE 13 may be supplemented as a result of furtherapplications of the methods of this invention.

TABLE 13 GABA GLUTAMINE PHENYLALANINE 300.00 N N N Anxiety Disorder NOS300.02 N N N Generalized Anxiety Disorder 300.22 N N N AgoraphobiaWithout History of Panic Disorder 300.23 N N N Social Phobia 300.29 N NN Specific Phobia 300.3 N N N Obsessive-Compulsive Disorder 309.81 N N NPosttraumatic Stress Disorder Panic N N N Disorder 299.00 N N AutisticDisorder

TABLE 13 containing a sample (additional data are attached in appendix 2to this disclosure) with novel treatments indicated by a table entry of“N.” Conventional treatment is indicated with an entry of “C” in theappropriate cell. The listings provided herein are not intended to be alimitation on the scope of the claimed invention. Instead it is anillustration of the utility of the invention. It also illustrates thatmany known agents are useful for treating traditionally diagnosedconditions. The failure to recognize such use is a reflection of limitedscreening methods available and the risks associated with them.

These individual diagnostic indications for use are, in preferred orparticular embodiments, conditioned on neurophysiologic (QEEG) data.Such conditions are preferably expressed as rules relevant to eachindication. A non-exhaustive list of such rules is presented in TABLE 6.Here, each row represents a rule formed by the (preferably fuzzy)disjunction of the multivariables in the indicated columns.

Further, these indications, although preferably applicable to patientswith behaviorally-diagnosed psychiatric conditions, may also apply topresently asymptomatic patients that display QEEG data (or, generally,quantified neurophysiologic data) that is otherwise indicated fortherapy. Such uses are referred to as “prophylactic.”

Administration of therapy is generally done in formulations and dosagesin accordance with known clinical and pharmaceutical guidelines. Forexisting therapeutic entities, already approved formulations may be usedin therapeutically effective dosages.

In more detail, the present invention encompasses the following specifictherapeutic aspects. The invention encompasses methods of establishingan indication for use of a therapeutic agent in treating patients havinga behaviorally diagnosed psychiatric disorder, wherein said agent hasnot heretofore been indicated for treatment of said disorder in approvedclinical practice, the method comprising: indicating said agent fortreatment of said disorder where quantified neurophysiologic dataobtained from one or more patients having said condition indicates thatsaid agent has been therapeutic effective in reference patients, whetheror not the reference patients have been diagnosed with said disorder.These methods includes treating a patient having a behaviorallydiagnosed psychiatric disorder other than an a disorder already approvedfor such therapy, and treating patients with particular indicateddiagnoses.

The invention further encompasses methods of recommending treatment fora patient having a behaviorally diagnosed psychiatric disorder,comprising: indicating one or more therapeutic agents in dependence onquantified neurophysiologic information obtained from said patient,wherein the therapeutic agents are indicated independently of theidentity of said disorder, and recommending one of more of the indicatedtherapeutic agents. The quantified information may includeneurophysiologic in formation, neuro-electro-physiologic information,neuro-electro-physiologic information obtained from said patient in aresting, un-stimulated condition, and may exclude patients withobservable systemic metabolic or anatomic pathology.

The invention further encompasses methods recommending treatment for apatient having a behaviorally diagnosed psychiatric disorder,comprising: indicating therapeutic agents by comparing quantifiedneurophysiologic information obtained from the patient with quantifiedneurophysiologic information obtained from individuals in one or morereference populations of individuals, wherein the information from atleast one reference population includes treatment modalities forindividuals with behaviorally diagnosed psychiatric disorders, andrecommending one or more of the indicated therapeutic agents. Theinvention further includes methods of recommending treatment for apatient having a behaviorally diagnosed psychiatric disorder,comprising: determining the effects of one or more therapeutic agents onquantified neurophysiologic information obtained from individuals in oneor more reference populations of individuals, and recommending one ormore therapeutic agents in dependence on a comparison of quantifiedneurophysiologic information obtained from said patient with saiddetermined effects of one or more therapeutic agents, whereintherapeutic agents are recommended independently of the identity of saiddisorder; as well as methods for correlating patients with therapeuticagents, wherein said patients have behaviorally diagnosed psychiatricdisorders, the method comprising: for each said patient and each saidagent, determining a level of correlation between said patient and saidagent by: indicating a relatively high level of correlation between saidpatient and said agent if quantified neurophysiologic informationobtained from said patient compares “closely” with quantifiedneurophysiologic information obtained from at least one referenceindividual of one or more reference populations of individuals, whereinthe information from at least one reference population includestreatment modalities for individuals, and wherein information for atleast one treatment modality for said reference individual indicatessaid reference individual was relatively effectively treated with saidagent, and indicating a relatively low level of correlation between saidpatient and said agent if quantified neurophysiologic informationobtained from said patient compares “closely” with quantifiedneurophysiologic information obtained from at least one referenceindividual of one or more reference populations of individuals, andwherein information for at least one treatment modality for saidreference individual indicates said reference individual was relativelyineffectively treated with said agent.

The invention may also be described by way of many embodimentsencompassed by it.

The invention encompasses a method for identifying an outcome of a firsttreatment based on neurophysiologic information from a subjectindependent of a behavioral mental disease diagnosis of or behavioraldata from the subject, the method comprising the steps of: scaling theneurophysiologic information to enable comparison with storedneurophysiologic information obtained from a data source; computing atleast one indicative variable from the neurophysiologic information; andevaluating the at least one indicative variable with aid of at least onerule to predict the outcome of the first treatment prior to actuallyadministering the first treatment. Optionally, the threshold number is80% whereby 80% of subjects having a common response to the firsttreatment are included in the cluster. Optionally, neurophysiologicinformation comprises electroencephalogram recordings recorded byelectrodes placed in accordance with the International 10/20 system.

Optionally, the method for identifying an outcome of a first treatmentbased on neurophysiologic information from a subject independent of abehavioral mental disease diagnosis of or behavioral data from thesubject further includes identifying the at least one indicativevariable by screening a response database comprising pre-treatmentneurophysiologic information and response to the first treatment in theform of active-treatment neurophysiologic information from a pluralityof subjects.

Optionally, the method for identifying an outcome of a first treatmentbased on neurophysiologic information from a subject independent of abehavioral mental disease diagnosis of or behavioral data from thesubject further includes identifying clusters of pre-treatmentneurophysiologic information associated with subjects having similarresponses to the first treatment as part of the screening step.

Optionally, the method for identifying an outcome of a first treatmentbased on neurophysiologic information from a subject independent of abehavioral mental disease diagnosis of or behavioral data from thesubject further includes identifying a cluster by identifying a regionin a multidimensional space defined by a range of values of unitaryvariables such that a threshold number of subjects having a commonresponse to the first treatment are included in the region; andidentifying the range of values of unitary variables describing theregion.

Optionally, the method for identifying an outcome of a first treatmentbased on neurophysiologic information from a subject independent of abehavioral mental disease diagnosis of or behavioral data from thesubject further includes combining the set of unitary variables havingvalues shared by subjects within a cluster to form a multivariable andemploying the multivariable as the at least one indicative variable.

Optionally, each of the similar responses is a clinical globalimprovement score selected from the set consisting of an integer in therange [−1 to 3] such that ‘−1’ indicates adverse therapeutic entityeffect, ‘0’ indicates no improvement, ‘1’ indicates minimal improvement,‘2’ indicates moderate improvement and ‘3’ indicates complete absence ofsymptoms. Optionally, each of the similar responses is a measure of thedifference between the active-treatment neurophysiologic information anda distribution of neurophysiologic information of age-matched referencesubjects.

Optionally, the method for identifying an outcome of a first treatmentbased on neurophysiologic information from a subject independent of abehavioral mental disease diagnosis of or behavioral data from thesubject further includes including the outcome of the first treatment ina report.

Optionally, the method for identifying an outcome of a first treatmentbased on neurophysiologic information from a subject independent of abehavioral mental disease diagnosis of or behavioral data from thesubject further includes applying a plurality of rules associated with aplurality of indicative variables to the neurological information from afirst data source; evaluating whether the rules indicate substantialagreement with one of a plurality of outcomes following the firsttreatment; and including, in response to such an indication, the one ofa plurality of outcomes following the first treatment in a report.

Optionally, the first treatment is specified in response to atraditional diagnosis of mental disease. Optionally, the first treatmentis in a list of treatments specified in response to the traditionaldiagnosis of mental disease whereby effective treatments in the list arerapidly identified.

Optionally, the method for identifying an outcome of a first treatmentbased on neurophysiologic information from a subject independent of abehavioral mental disease diagnosis of or behavioral data from thesubject further includes comparing a result of applying at least onerule to the neurological information from the subject to at least oneexpected result associated with a second treatment, the second treatmentnot in the list of treatments based on the neurological information fromthe subject; and identifying, in response to detecting a similaritybetween the at least one expected result and the result, the secondtreatment as a possible treatment in a report.

Optionally, the traditional diagnosis is major depressive disorder andthe second treatment is selected from the group consisting of glutamine,phenylalanine, and tyrosine, or the traditional diagnosis ispsychological factors affecting medical condition, atypical asthma andthe second treatment is selected from the group consisting of glutamine,phenylalanine, tyrosine, bupropion, parnate, moclobemide, phenalzine,seligeline, venlafaxine, carbamazapine, gabapentin, lamotrigine, ginkobiloba, dexedrine, methapmphetamine, methylphenidate, and pemoline.

Optionally, the traditional diagnosis may be one of anxiety disordersand the second treatment is selected from the group consisting of gaba,glutamine, phenylalanine, tyrosine, buproprion, citalopram, fluvoxamine,citalopramine, clomipramine, moclobemide, parnate, phenalzine,seligeline, carbamazapine, divalproex, gabapentin, lamotrigine,guanfacine hcl, clonidine, atenolol, metopolol, propranolol, lithium,ginko biloba, kava kava, st. john's wort, amantadine, phototherapy at10000 lux, adderall, dexedrine, methapmphetamine, methylphenidate,modafinil, and pemoline.

Optionally, the traditional diagnosis may be one of psychologicalfactors affecting medical condition, disorders usually first diagnosedin infancy, childhood, or adolescence and the second treatment isselected from the group consisting of gaba glutamine, phenylalanine,tyrosine, donepezil, buproprion, citalopram, clomiprimine, doxepin,fluoxetine, fluvoxamine, moclobemide, parnate, phenalzine, seligeline,trazodone, venlafaxine, carbamazapine, diphenylhydantoin, divalproex,gabapentin, lamotrigine, guanfacine hcl, clorazepate, diazapam,oxazepam, quazepam, atenolol, metopolol, propranolol, lithium, ginkobiloba, kava kava, st. john's wort, silbtrimin, amantadine, phototherapyat 10000 lux, adderall, dexedrine, methapmphetamine, methylphenidate,modafinil, and phentermine.

Optionally, the traditional diagnosis may be one of eating disorders andthe second treatment is selected from the group consisting of gaba,glutamine, phenylalanine, tyrosine, donepezil, buproprion, moclobemide,parnate, phenalzine, seligeline, venlafaxine, carbamazapine,diphenylhydantoin, divalproex, gabapentin, lamotrigine, diazapam,lorazepam, atenolol, metopolol, propranolol, lithium, ginko biloba, kavakava, st. john's wort, amantadine, phototherapy at 10000 lux, zolipidem,adderall, dexedrine, methapmphetamine, methylphenidate, modafinil,pemoline, and phentermine.

Optionally, the traditional diagnosis may be one of delirium, dementiaand amnestic and other cognitive disorders and the second treatment isselected from the group consisting of glutamine, phenylalanine,tyrosine, donepezil, amitriptyline, buproprion, fluxotine, moclobemide,parnate, phenalzine, seligeline, venlafaxine, carbamazapine, divalproex,gabapentin, lamotrigine, atenolol, metopolol, propranolol, lithium,ginko biloba, silbtrimin, amantadine, phototherapy at 10000 lux,zolipidem, adderall, dexedrine, methapmphetamine, methylphenidate,modafinil, pemoline, and phentermine.

Optionally, the traditional diagnosis may be impulse control disordersnot elsewhere classified and the second treatment is selected from thegroup consisting of glutamine, phenylalanine, tyrosine, donepezil,buproprion, citalopram, clomiprimine, desipramine, moclobemide,nefazodone, parnate, phenalzine, seligeline, venlafaxine, carbamazapine,diphenylhydantoin, divalproex, gabapentin, lamotrigine, guanfacine hcl,clonidine, atenolol, metopolol, propranolol, ginko biloba, kava kava,silbtrimin, amantadine, phototherapy at 10000 lux, adderall, dexedrine,methapmphetamine, methylphenidate, and pemoline.

Optionally, the traditional diagnosis may be one of mood disorders andthe second treatment is selected from the group consisting of glutamine,phenylalanine, tyrosine, moclobemide, parnate, phenalzine, seligeline,diphenylhydantoin, lamotrigine, guanfacine hcl, clonidine, lorazepam,oxazepam, quazepam, temazepam, trizolam, atenolol, metopolol,propranolol, ginko biloba, kava kava, st. john's wort, phototherapy at10000 lux, adderall, dexedrine, methapmphetamine, methylphenidate,pemoline, and phentermine.

Optionally, the traditional diagnosis may be one of other codes andconditions and the second treatment is selected from the groupconsisting of gaba, glutamine, phenylalanine, tyrosine, donepezil,buproprion, citalopram, clomiprimine, fluvoxamine, moclobemide,notriptyline, parnate, phenalzine, seligeline, trazodone, venlafaxine,carbamazapine, divalproex, gabapentin, lamotrigine, guanfacine hcl,clonidine, atenolol, metopolol, propranolol, ginko biloba, kava kava,st. john's wort, amantadine, phototherapy at 10000 lux, zolipidem,adderall, dexedrine, methapmphetamine, methylphenidate, pemoline, andphentermine.

Optionally, the traditional diagnosis may be one of personalitydisorders and the second treatment is selected from the group consistingof gaba, glutamine, phenylalanine, tyrosine, donepezil, buproprion,moclobemide, parnate, phenalzine, seligeline, venlafaxine,carbamazapine, diphenylhydantoin, divalproex, gabapentin, lamotrigine,diazapam, atenolol, metopolol, propranolol, lithium, ginko biloba, kavakava, st. john's wort, phototherapy at 10000 lux, adderall, dexedrine,methapmphetamine, methylphenidate, pemoline, and phentermine.

Optionally, the traditional diagnosis may be hypoactive sexual desiredisorder and the second treatment is selected from the group consistingof buproprion, buspirone, moclobemide, parnate, phenalzine, andseligeline.

Optionally, the traditional diagnosis may be one of sleep disorders andthe second treatment is selected from the group consisting of gaba,glutamine, phenylalanine, tyrosine, donepezil, buproprion, buspirone,citalopram, clomiprimine, desipramine, fluoxetine, fluvoxamine,moclobemide, parnate, phenalzine, seligeline, sertraline, venlafaxine,carbamazapine, diphenylhydantoin, divalproex, gabapentin, lamotrigine,guanfacine hcl, clonidine, atenolol, metopolol, propranolol, lithium,ginko biloba, kava kava, st. john's wort, silbtrimin, phototherapy at10000 lux, adderall, dexedrine, methapmphetamine, methylphenidate,pemoline, and phentermine.

Optionally, the traditional diagnosis may be one of somatoform disordersand the second treatment is selected from the group consisting of gaba,glutamine, phenylalanine, tyrosine, donepezil, buproprion, citalopram,fluvoxamine, moclobemide, parnate, phenalzine, seligeline,carbamazapine, diphenylhydantoin, divalproex, gabapentin, lamotrigine,atenolol, metopolol, propranolol, ginko biloba, kava kava, st. john'swort, amantadine, phototherapy at 10000 lux, zolipidem, adderall,dexedrine, methapmphetamine, methylphenidate, modafinil, pemoline, andphentermine.

Optionally, the traditional diagnosis may be one of substance-relateddisorders and the second treatment is selected from the group consistingof gaba, glutamine, phenylalanine, tyrosine, donepezil, fluvoxamine,moclobemide, parnate, phenalzine, seligeline, venlafaxine,carbamazapine, diphenylhydantoin, divalproex, gabapentin, lamotrigine,guanfacine hcl, atenolol, metopolol, propranolol, ginko biloba, kavakava, st. john's wort, silbtrimin, phototherapy at 10000 lux, adderall,dexedrine, methapmphetamine, methylphenidate, and pemoline.

Optionally, the first treatment in the list of treatments is identifiedas unlikely to result in a favorable outcome. Optionally, the method foridentifying an outcome of a first treatment based on neurophysiologicinformation from a subject independent of a behavioral mental diseasediagnosis of or behavioral data from the subject further includesdisplaying additional treatments, based on the neurophysiologicinformation from the subject, for obtaining the desired response.

Optionally, the method for identifying an outcome of a first treatmentbased on neurophysiologic information from a subject independent of abehavioral mental disease diagnosis of or behavioral data from thesubject further includes transmitting neurophysiologic information, overa communication link, to a remote site for analysis; and receiving aresponse thereto. Optionally, the response is provided within a timeinterval suitable for concurrent examination of a subject and treatment.

The invention also encompasses a method for identifying a treatment fora subject based on pretreatment neurophysiologic information from thesubject and a desired outcome, the method comprising the steps of:scaling the pretreatment neurophysiologic information to enablecomparison with stored neurophysiologic information obtained from a datasource; constructing clusters of pretreatment neurophysiologicinformation in a treatment-response database comprising pre-treatmentneurophysiologic information and associated response score andactive-treatment neurophysiologic information for each of a plurality ofsubjects by considering pretreatment neurophysiologic informationassociated with the desired outcome; identifying at least one cluster towhich the pretreatment neurophysiologic information of the subjectbelongs, the at least one cluster defining a range of neurophysiologicinformation; and identifying at least one treatment associated with theat least one cluster. Optionally, neurophysiologic information compriseselectroencephalogram recordings recorded by electrodes placed inaccordance with the International 10/20 system.

Optionally, the method for identifying a treatment for a subject basedon pretreatment neurophysiologic information from the subject and adesired outcome further includes listing treatments associated with theat least one cluster.

Optionally, the method for identifying a treatment for a subject basedon pretreatment neurophysiologic information from the subject and adesired outcome further includes listing treatments associated with eachcluster to which the pretreatment neurophysiologic information of thesubject belongs.

Optionally, the method for identifying a treatment for a subject basedon pretreatment neurophysiologic information from the subject and adesired outcome further includes specifying at least onecluster-defining rule. Optionally, the at least one cluster-definingrule specifies that each cluster, associated with at least onetreatment, includes at least 80% of subjects having pretreatmentneurological information associated with the desired outcome.Optionally, the at least one cluster-defining rule further specifiesthat preferably no more than 10%, even more preferably 15%, and mostpreferably 20% of subjects having pretreatment neurophysiologicinformation within bounds of each cluster, associated with at least onetreatment, are associated with a treatment different than thatassociated with the each cluster. Optionally, the at least onecluster-defining rule further specifies that false positives do notexceed a threshold

Optionally, the method for identifying a treatment for a subject basedon pretreatment neurophysiologic information from the subject and adesired outcome further includes receiving pretreatment neurophysiologicinformation from a remote location over a communication link.

Optionally, the method for identifying a treatment for a subject basedon pretreatment neurophysiologic information from the subject and adesired outcome further includes sending a message disclosing the atleast one treatment over a communication link to a remote location.

Optionally, the method for identifying a treatment for a subject basedon pretreatment neurophysiologic information from the subject and adesired outcome further includes screening a plurality of subjectshaving respective pre-treatment neurophysiologic information in the samecluster for a common genetic determinant. Optionally, at least some ofthe plurality of subjects are related genetically by membership in afamily-tree spanning at least two generations and no more than twentygenerations.

The invention also encompasses a method of building a treatment-responsedatabase to facilitate predicting treatments having a desirable outcome,avoiding ineffective or harmful treatments, and defining treatment-basedconditions, the method comprising the steps of: storing initialneurophysiologic information associated with a subject in associationwith a treatment administered to the subject, a active-treatmentneurophysiologic information associated with the subject and amagnitude-outcome of the treatment associated with the subject, themagnitude-outcome reflecting the extent of change rather than change ina particular feature whereby effect of the treatment on different mentaldiseases having various distinct features can be compared; and obtainingsuch information from at least a specified number of subjects.

Optionally, the method of building a treatment-response database tofacilitate predicting treatments having a desirable outcome, avoidingineffective or harmful treatments, and defining treatment-basedconditions further includes computing the magnitude-outcome of thetreatment associated with the subject to the treatment by comparing theactive-treatment neurophysiologic information to the initialneurophysiologic information associated with the subject.

Optionally, the initial neurophysiologic information is pretreatmentneurophysiologic information corresponding to a treatment-free state ofthe subject. Optionally, the treatment-free state of the subjectrequires that the subject not be administered the treatment for a priortime duration of at least seven and a half half-lives of the treatmentwhereby eliminating prior effects of the treatment.

Optionally, the method of building a treatment-response database tofacilitate predicting treatments having a desirable outcome, avoidingineffective or harmful treatments, and defining treatment-basedconditions further includes entering, in the treatment-responsedatabase, an identifier for a cluster of initial neurophysiologicinformation whereby enabling subsequent searching of thetreatment-response database for at least one cluster of initialneurophysiologic information similar to a query initial neurophysiologicinformation.

Optionally, the method of building a treatment-response database tofacilitate predicting treatments having a desirable outcome, avoidingineffective or harmful treatments, and defining treatment-basedconditions further includes identifying an initial neurophysiologicprofile in a neurophysiologic information entry; identifying a treatmentadministered to a subject associated with the neurophysiologicinformation entry; and identifying a magnitude-outcome of the treatmentcorresponding to the subject associated with the neurophysiologicinformation entry whereby adding a neurophysiologic information entry ofa new subject to the treatment-response database.

Optionally, the method of building a treatment-response database tofacilitate predicting treatments having a desirable outcome, avoidingineffective or harmful treatments, and defining treatment-basedconditions further includes determining whether a subject associatedwith the neurophysiologic entry satisfies a threshold criterion.

The invention also encompasses a treatment-response database comprising:initial neurophysiologic information for each of a plurality ofsubjects; treatment information for the each of a plurality of subjects;and indicator of clinical treatment outcome for the each of a pluralityof subjects. Optionally, the plurality of subjects number at least onehundred subjects.

Optionally, the treatment-response database further includes anidentifier associated with at least one cluster of pretreatmentneurophysiologic information wherein the at least one cluster includespretreatment neurophysiologic information from subjects having similarresponses to a treatment.

The invention also encompasses a method for identifying a condition forwhich a treatment is available, the method comprising the steps of:obtaining initial neurophysiologic information from a plurality ofsubjects; obtaining active-treatment neurophysiologic information forthe plurality of subjects following administration to each of theplurality of subjects a treatment; obtaining an outcome for each of theplurality of subjects following the treatment; clustering initialneurophysiologic information from subjects exhibiting a desirableoutcome following the treatment to obtain at least one cluster, whereina cluster is bounded by values of neurophysiologic information; andidentifying a range of values of neurophysiologic information definingthe at least one cluster as a condition precedent to be satisfied by anew initial neurophysiologic information of a new subject prior toadministration of the treatment.

Optionally, the method for identifying a condition for which a treatmentis available further includes specifying a threshold for defining acluster. Optionally, the at least one cluster has no more than athreshold fraction of false positives whereby limiting subjects havinginitial neurophysiologic information falling within the at least onecluster although the subjects do not exhibit the desirable outcomefollowing the treatment.

Optionally, the method for identifying a condition for which a treatmentis available further includes identifying the range of values ofneurophysiologic information as a condition responsive to the treatment.

Optionally, the method for identifying a condition for which a treatmentis available further includes diagnosing a new subject as afflicted withthe condition responsive to the treatment based on an initialneurophysiologic information of the new subject falling within the atleast one cluster.

Optionally, the method for identifying a condition for which a treatmentis available further includes estimating the fraction of the pluralityof subjects having initial neurophysiologic information falling withinthe at least one cluster to estimate the number of people in the UnitedStates that are responsive to the treatment. Optionally, estimatingincludes employing a sampling frequency associated with the plurality ofsubjects. Optionally, the method further includes determining whetherthe number of people in the United States that are responsive to thetreatment is less than a qualifying threshold. Optionally, thequalifying threshold is 200,000.

The invention also encompasses a method for estimating a function of atherapeutic entity on a subject of interest, the method comprising thesteps of: receiving a neurophysiologic information of the subject;identifying clusters of neurophysiologic information, each of theclusters defined by a range of values for neurophysiologic information,in a treatment-response database comprising neurophysiologic informationand the effect of treatments thereon, such that the neurophysiologicinformation of the subject satisfies respective ranges of the identifiedclusters; identifying treatments associated with the identifiedclusters; determining whether any of the treatments is similar to anadministration of the therapeutic entity; and inferring the function ofthe therapeutic entity based on the function of the identifiedtreatments.

Optionally, the method for estimating a function of a therapeutic entityon a subject of interest further includes inferring lack of a desirableeffect of the therapeutic entity on the subject in response to a failureto identify a treatment similar to the therapeutic entity in clustersadditionally associated with the desirable effect in thetreatment-response database.

Optionally, the method for estimating a function of a therapeutic entityon a subject of interest further includes transmitting neurophysiologicinformation to a remote site for analysis; and receiving a responsethereto.

The invention also encompasses a method for reevaluating therapeuticentity testing data, that does not reveal a desired effect of atherapeutic entity on subjects, to identify at least one condition forusing the therapeutic entity on at least one subset of subjects, themethod comprising the steps of: identifying subjects having initialneurophysiologic information and a desired response to the therapeuticentity in the therapeutic entity testing data; clustering initialneurophysiologic information corresponding to the subjects having adesirable response to administration of the therapeutic entity;identifying at least one cluster that satisfies at least one of the setconsisting of a prescribed threshold; identifying a range of a parameterdefining the at least one cluster; and specifying the range of theparameter as a condition for pre-screening subjects for administrationof the therapeutic entity whereby ensuring that subjects foradministering the therapeutic entity also have neurophysiologicinformation belonging to the at least one cluster.

Optionally, the prescribed threshold is selected from the set consistingof a number of false positives, a number of false negatives, and a ratioof false positives to false negatives. Optionally, the therapeuticentity is known to be safe in humans. Optionally, the therapeutic entityis known to have at least one known use. Optionally, the therapeuticentity testing data relates to identifying additional applications ofthe therapeutic entity.

Optionally, the method for reevaluating therapeutic entity testing data,that does not reveal a desired effect of a therapeutic entity onsubjects, to identify at least one condition for using the therapeuticentity on at least one subset of subjects further includes estimatingthe at least one subset of subjects as a fraction of the subjects toestimate the number of people in a jurisdiction that are responsive tothe treatment. Optionally, estimating includes employing a samplingfrequency associated with the plurality of subjects.

Optionally, the method for reevaluating therapeutic entity testing data,that does not reveal a desired effect of a therapeutic entity onsubjects, to identify at least one condition for using the therapeuticentity on at least one subset of subjects further includes determiningwhether the number of people in the United States that are responsive tothe treatment is less than a qualifying threshold. Optionally, thequalifying threshold is 200,000.

The invention also encompasses a method for generating rules forpredicting suitability of a treatment for a subject based on thesubject's neurophysiologic information as opposed to a traditionaldiagnosis of a mental disorder, the method comprising the steps of:clustering initial neurophysiologic information from a plurality ofsubjects such that each cluster is associated with at least onetreatment outcome; evaluating neurophysiologic information in a clusterto determine at least one feature of the neurophysiologic informationthat is common to the cluster; and generating a rule based on the atleast one feature to determine whether a new initial neurophysiologicinformation from a new subject belongs to the cluster whereby predictingthe same outcome for the treatment as that associated with the cluster.

Optionally, neurophysiologic information is collected using aneurophysiologic technique selected from the set consisting ofelectroencephalograhy, evoked potentials, event-related potentials,direct electrode recordings, magnetic resonance imaging, positronemission tomography, single photon emission computerized tomography,electromagnetocephalography and any combination thereof. Optionally, theneurophysiologic information is in the form of unitary variables thatdefine a multidimensional space such that a cluster occupies acontiguous region defined by values of unitary variables therein.

Optionally, the method for generating rules for predicting suitabilityof a treatment for a subject based on the subject's neurophysiologicinformation as opposed to a traditional diagnosis of a mental disorderfurther includes describing the cluster by the feature comprising atleast one of the multivariables from the set consisting of EEG absolutepower average, Frontal Midline Progression Index, Posterior MidlineProgression Index, Ratio of Frontal/Posterior Alpha Indices, AverageMidline Theta/Beta ratio, RMAD, RMPD, RMAT, RMPT, RMAA, RMPA, RMAB,RMPB, CEAD, CEPD, CEAT, CEPT, CEAA, CEPA, CEAB, CEPB, FMAD, FMPD, FMAT,FMPT, FMAA, FMPA, FMAB, FMPB, AADL, AADR, AATL, AATR, AAAL, AAAR, AABL,AABR, AED, AET, AEA, AEB, AEBD, AEBT, AEBA, AEBB, CADL, CADR, CATL,CATR, CAAL, CAAR, CABL, CABR, CEBD, CEBT, CEBA, CEBB, RBDL, RBDR, RBTL,RBTR, RBAL, RBAR, RBBL, and RBBR.

Optionally, the method for generating rules for predicting suitabilityof a treatment for a subject based on the subject's neurophysiologicinformation as opposed to a traditional diagnosis of a mental disorderfurther includes describing the cluster by specifying a range for eachof the features: EEG absolute power average, Posterior MidlineProgression Index, Ratio of Frontal/Posterior Alpha Indices, AverageMidline Theta/Beta ratio, RMAB, RMPB, CEAA, CEPA, CEAB, CEPB, FMAA,FMPA, FMAB, FMPB, CAAL, CAAR, CABL, CABR, CEBA, and CEBB.

Optionally, the method for generating rules for predicting suitabilityof a treatment for a subject based on the subject's neurophysiologicinformation as opposed to a traditional diagnosis of a mental disorderfurther includes identifying the new initial neurophysiologicinformation from the new subject as belonging to the cluster in responseto determining a substantial correlation between the new initialneurophysiologic information and ranges for the features describing thecluster.

The invention also encompasses a method of using a treatment-responsedatabase comprising a treatment, initial neurophysiologic information,active-treatment neurophysiologic information and an outcome of thetreatment, the method comprising the steps of: converting intounivariate measures; extracting multivariables of interest from theunivariate measures; and storing multivariables in thetreatment-response database whereby facilitating subsequent databasesearches.

The invention also encompasses a portable device for evaluating andsuggesting a treatment, the device comprising: an input module forreceiving neurophysiologic information from a subject; a rule module forproviding rules for a specific variables in the neurophysiologicinformation; a correspondence module to detect a match between a resultof applying rules to variables in the neurophysiologic information andthe expected result for a treatment; and an output module for indicatingan outcome for at least one treatment.

Optionally, the neurophysiologic information comprises a plurality ofunivariate variables and the specific variable includes at least oneunivariate variable. Optionally, the portable device further includes atleast one reference distribution for scaling the neurophysiologicinformation with respect thereto. Optionally, the portable devicefurther includes a treatment-response database to facilitate predictingtreatments having a desirable outcome, avoiding ineffective or harmfultreatments, and defining treatment-based conditions by undertakingreanalysis of data therein.

The invention also encompasses a method of establishing an approved useof a therapeutic agent in treating patients having a disorder, whereinsaid agent has not heretofore been approved for treatment of saiddisorder in approved clinical practice, the method comprising:indicating said agent for treatment of said disorder whereneurophysiologic information obtained from one or more patients havingsaid condition indicates that said agent has therapeutic effectivenessin reference patients, whether or not the reference patients have beendiagnosed with said disorder.

Optionally, the method further includes administering a therapeuticallyeffective amount of said indicated agent to one or more patients, andverifying that said agent is effective in at least one patient.Optionally, the method further includes administering a therapeuticallyeffective amount of an agent indicated by the method of claim 87 to beeffective in treating patients with said disorder. The method includesscenarios wherein said behaviorally diagnosed disorder is anorexianervosa, bulimia nervosa, or other eating disorder, and wherein saidagent is selected from the group consisting of methylphenidate anddextroamphetamine. The method also includes scenarios of treating apatient having a behaviorally diagnosed psychiatric disorder other thanan attention-deficit/hyperactivity disorder, comprising: administering atherapeutically effective dose of methylphenidate.

The invention encompasses a method of treating a patient havingbehaviorally diagnosed anorexia nervosa, bulimia nervosa, or othereating disorder, comprising: administering a therapeutically effectiveamount of a drug selected from the group consisting of methylphenidateand dextroamphetamine.

The invention also encompasses a method of recommending treatment for apatient having a behaviorally diagnosed psychiatric disorder,comprising: indicating one or more therapeutic agents in dependence onneurophysiologic information obtained from said patient, wherein thetherapeutic agents are indicated independently of the identity of saiddisorder, and recommending one of more of the indicated therapeuticagents wherein said patient is without externally observable anatomicpathology.

Optionally, the indicated one or more therapeutic agents comprise agentsfrom a single class of agents, wherein a class of agents comprisesagents with similar physiological effects on a target organ system.Optionally, the class of agents is selected from the group consisting ofclass 1 agents, class 2 agents, class 3 agents, class 4 agents, andclass 5 agents.

The method also encompasses treating a patient having a behaviorallydiagnosed psychiatric disorder, comprising: administering one or morerecommended therapeutic agents.

The invention also encompasses a method of recommending treatment for apatient having a behaviorally diagnosed psychiatric disorder,comprising: indicating therapeutic agents by comparing quantifiedneurophysiologic information obtained from the patient with quantifiedneurophysiologic information obtained from individuals in one or morereference populations of individuals, wherein the information from atleast one reference population includes treatment modalities forindividuals with behaviorally diagnosed psychiatric disorders, andrecommending one or more of the indicated therapeutic agents.

Optionally, the method includes administering one or more recommendedtherapeutic agents. Optionally, the method includes scenarios whereinthe behavioral diagnosis comprises a diagnosis made according toprofessionally accepted psychiatric criteria.

The invention also encompasses a method of recommending treatment for apatient having a behaviorally diagnosed psychiatric disorder,comprising: determining the effects of one or more therapeutic agents onquantified neurophysiologic information obtained from individuals in oneor more reference populations of individuals, and recommending one ormore therapeutic agents independence on a comparison of quantifiedneurophysiologic information obtained from said patient with saiddetermined effects of one or more therapeutic agents, whereintherapeutic agents are recommended independently of the identity of saiddisorder.

Optionally, the comparison indicates a therapeutic agent if thedetermined effects of said agent substantially correct abnormalities insaid neurophysiologic information obtained from said patient.Optionally, the method for treating a patient having a behaviorallydiagnosed psychiatric disorder, includes administering one or morerecommended therapeutic agents.

The invention also encompasses a method of correlating patient withtherapeutic agents, wherein said patients have behaviorally diagnosedpsychiatric disorders, the method comprising: for each said patient andeach said agent, determining a level of correlation between said patientand said agent by indicating a relatively high level of correlationbetween said patient and said agent if quantified neurophysiologicinformation obtained from said patient correlates with quantifiedneurophysiologic information obtained from at least one referenceindividual of one or more reference populations of individuals, whereinthe information from at least one reference population includestreatment modalities for individuals, and wherein information for atleast one treatment modality for said reference individual indicatessaid reference individual was relatively effectively treated with saidagent, and indicating a relatively low level of correlation between saidpatient and said agent if quantified neurophysiologic informationobtained from said patient correlates with quantified neurophysiologicinformation obtained from at least one reference individual of one ormore reference populations of individuals, and wherein information forat least one treatment modality for said reference individual indicatessaid reference individual was relatively ineffectively treated with saidagent.

Optionally, the invention encompasses a method of recommending treatmentfor a patient having a behaviorally diagnosed psychiatric disorderincludes recommending agents correlated with said patient in accordancewith the method of correlating patient with therapeutic agents.

Optionally, the invention encompasses a method of recommending a patientfor a trial of a therapeutic agent-in-trial includes recommendingpatients correlated with at least one similar therapeutic agentaccording to the method of correlating patient with therapeutic agents,and wherein an agent is similar to said agent-in-trial if the effects ofsaid agent and said agent-in-trial on quantified neurophysiologicinformation obtained from individuals in one or more referencepopulations of individuals compares closely.

The invention also encompasses a method for classifying physiologicbrain imbalances, comprising: comparing quantified neurophysiologicinformation from a patient with neurophysiologic information from areference population of individuals to produce a group of differencesfor the patient, organizing said differences by neurophysiologic outputmeasurements to provide a differences profile of the physiological stateof the patient's brain function, comparing said differences profile ofthe patient with neurophysiologic information from a second referencepopulation who are symptomatic for physiologic brain imbalances toproduce a group of similarities for the patient, organizing saidsimilarities by neurophysiologic output measurements to provide asimilarities profile of the physiological state of the patient's brainfunction, correlating said similarities profile of the patient with aseries of treatment modalities for the second reference group to producea treatment recommendation.

Optionally, the treatment modality is drug therapy, and wherein the drugis selected from the group consisting of alprazolam, amantadine,amitriptyline, atenolol, bethanechol, bupropion, buspirone,carbamazepine, chlorpromazine, chlordiazepoxide, citalopram,clomipramine, clonidine, clonazepam, clozapine, cyproheptadine,dexamethasone, divalproex, deprenyl, desipramine, dexamethasone,dextroamphetamine, diazepam, disulfram, divalproex, doxepin,ethchlorvynol, fluoxetine, fluvoxamine, felbamate, fluphenazine,gabapentin, haloperidol, imipramine, isocarboxazid, lamotrigine,levothyroxine, liothyronine, lithium carbonate, lithium citrate,lorazepam, loxapine, maprotiline, meprobamate, mesoridazine,methamphetamine, midazolam, meprobamate, mirtazapine, molindone,moclobemide, molindone, naltrexone, phenelzine, nefazodone,nortriptyline, olanzapine, oxazepam, paroxetine, pemoline, perphenazine,phenelzine, pimozide, pindolol, prazepam, propranolol, protriptyline,quetiapine, reboxetine, risperidone, selegiline, sertraline, sertindole,trifluoperazine, trimipramine, temazepam, thioridazine, topiramate,tranylcypromine, trazodone, triazolam, trihexyphenidyl, trimipramine,valproic acid, venlafaxine, and any combination thereof.

Optionally, the physiologic brain imbalance accompanies panic disorderand the treatment modality is drug therapy using a drug selected fromthe group consisting of valproic acid, clonazepam, carbamazepine,methylphenidate and dextroamphetamine.

Optionally, the physiologic brain imbalance accompanies eating disorderand the treatment modality is drug therapy using a drug selected fromthe group consisting of methylphenidate and dextroamphetamine.

Optionally, the physiologic brain imbalance accompanies learningdisorder and the treatment modality is drug therapy using a drugselected from the group consisting of amantadine, valproic acid,clonazepam and carbamazepine.

Optionally, the method includes obtaining follow-up neurophysiologicinformation to track physiologic changes produced by the administrationof treatment modalities, and making therapy regime changes based on thefollow-up neurophysiologic information and a patient assessment tool.

Optionally, the method includes scenarios wherein the physiologic brainimbalance is associated with behaviorally or non-behaviorally diagnosedbrain pathologies.

Optionally, the method includes scenarios wherein the brain pathology isselected from the group consisting of agitation, Attention DeficitHyperactivity Imbalance, Abuse, Alzheimer's disease/dementia, anxiety,panic, and phobic disorders, bipolar disorder, borderline personalitydisorder, behavior control problems, body dysmorphic disorders,cognitive problems, Creutzfeldt-Jakob disease, depression, dissociativedisorders, eating, appetite, and weight problems, edema, fatigue,hiccups, impulse-control problems, irritability, jet lag, mood problems,movement problems, obsessive-compulsive disorder, pain, personalityimbalances, posttraumatic stress disorder, schizophrenia and otherpsychotic disorder, seasonal affective disorder, sexual disorder, sleepdisorder, stuttering, substance abuse, tic disorder/Tourette's Syndrome,traumatic brain injury, Trichotillomania, Parkinson's disease,violent/self-destructive behaviors, and any combination thereof.

The invention encompasses a method for classifying physiologic brainimbalances, comprising: comparing quantified neurophysiologicinformation from a patient with neurophysiologic information from areference population of individuals to produce a group of differencesfor the patient, and organizing the differences by neurophysiologicoutput measurements to provide a differences profile of thephysiological state of the patient's brain function.

Optionally the method for classifying physiologic brain imbalancesincludes scenarios wherein the quantified neurophysiologic informationis fast Fourier transform quantitative electroencephalography.

Optionally the method for classifying physiologic brain imbalancesincludes scenarios wherein the quantified neurophysiologic informationis nonparoxysmal.

Optionally the method for classifying physiologic brain imbalancesincludes scenarios wherein the quantified neurophysiologic informationis at least in part paroxysmal.

Optionally the method for classifying physiologic brain imbalancesincludes scenarios wherein the neurophysiologic information is generalor FFT quantitative electroencephalography (QEEG) information.

Optionally the method for classifying physiologic brain imbalancesincludes scenarios wherein the quantified neurophysiologic informationfrom a patient and from a reference population is general or FFT QEEGmultivariate output measurements.

Optionally the method for classifying physiologic brain imbalancesincludes scenarios wherein the general or FFT QEEG multivariate outputmeasurements are selected from a group consisting of absolute power,relative power, frequency, intrahemispheric coherence, interhemisphericcoherence, intrahemispheric asymmetry, and interhemispheric asymmetry,and ratios or combinations thereof.

Optionally the method for classifying physiologic brain imbalancesincludes scenarios wherein the general or FFT QEEG multivariate outputmeasurements are determined from combinations of EEG electrodes found inthe anterior, posterior, right hemisphere, left hemisphere regions ofthe scalp.

Optionally the method for classifying physiologic brain imbalancesincludes scenarios wherein the general or FFT QEEG multivariate outputmeasurements are determined from electrodes or combinations ofelectrodes in the delta, theta, alpha, or beta EEG frequency bands.

Optionally the method for classifying physiologic brain imbalancesincludes scenarios wherein Z scores are determined for each general orFFT QEEG multivariate output measurement.

Optionally the method for classifying physiologic brain imbalancesincludes scenarios wherein the general or FFT QEEG multivariate outputmeasurements are expressed in terms of Z scores.

Optionally the method for classifying physiologic brain imbalancesincludes scenarios wherein the reference population is drawn fromindividuals who are asymptomatic for physiologic brain imbalances.

Optionally, the invention also encompasses a method for treatingphysiologic brain imbalances of a patient, comprising correlating thedifferences profile of the patient according to the method forclassifying physiologic brain imbalances with a series of treatmentmodalities to produce a treatment recommendation.

The invention also encompasses a method for analyzing physiologic brainimbalances of a patient, comprising: comparing the differences profileof the patient according to claim 104 with neurophysiologic informationfrom a second reference population of individuals who are symptomaticfor physiologic brain imbalances to produce a group of similarities forthe patient; and organizing the similarities by neurophysiologic outputmeasurements to provide a similarities profile of the physiologicalstate of the patient's brain function.

Optionally, the invention also encompasses a method for treatingphysiologic brain imbalances of a patient, comprising: correlating thesimilarities profile of the patient according to the method foranalyzing physiologic brain imbalances of a patient with a series oftreatment modalities for the second reference group to produce atreatment recommendation.

The invention also encompasses a method for analyzing physiologic brainimbalances of a patient, comprising: comparing quantifiedneurophysiologic information from the patient with neurophysiologicinformation from a reference population of individuals who aresymptomatic for physiologic brain imbalances to produce a group ofsimilarities for the patient, and organizing the similarities byneurophysiologic output measurements to provide a similarities profileof the physiological state of the patient's brain function.

Optionally, the method for analyzing physiologic brain imbalances of apatient includes scenarios wherein the symptomatic patients from whomthe neurophysical output measurements are collected exhibit behavioralindicia of physiologic brain imbalances.

Optionally, the method for analyzing physiologic brain imbalances of apatient includes scenarios wherein the symptomatic patients from whomthe neurophysiologic output measurements are collected exhibitnon-behavioral indicia of physiologic brain imbalances.

The invention also encompasses a method for treating physiologic brainimbalances of a patient, comprising: correlating the similaritiesprofile of the patient according to the method for analyzing physiologicbrain imbalances of a patient with a series of treatment modalities forthe reference group to produce a treatment recommendation.

The invention also encompasses a method for classifying physiologicbrain imbalances, comprising: comparing quantified neurophysiologicinformation from a patient with neurophysiologic information from areference population of individuals to produce a group of differencesfor the patient; and organizing the differences by neurophysiologicoutput measurements to provide a differences profile of thephysiological state of the patient's brain function.

The invention also encompasses a method for analyzing physiologic brainimbalances of a patient, comprising: comparing the differences profileof the patient with neurophysiologic information from a second referencepopulation who are symptomatic for physiologic brain imbalances toproduce a group of similarities for the patient; and organizing thesimilarities by neurophysiologic output measurements to provide asimilarities profile of the physiological state of the patient's brainfunction.

The invention also encompasses a method for treating the analyzedphysiologic brain imbalances of a patient, comprising correlating thesimilarities profile of the patient with a series of treatmentmodalities for the second reference group to produce a treatmentrecommendation.

The invention also encompasses a method wherein the analyzed physiologicbrain imbalance is associated with behaviorally or non-behaviorallydiagnosed brain pathologies. Optionally, the brain pathology is selectedfrom the group consisting of agitation, Attention Deficit HyperactivityImbalance, Abuse, Alzheimer's disease/dementia, anxiety, panic, andphobic disorders, bipolar disorder, borderline personality disorder,behavior control problems, body dysmorphic disorders, cognitiveproblems, Creutzfeldt-Jakob disease, depression, dissociative disorders,eating, appetite, and weight problems, edema, fatigue, hiccups,impulse-control problems, irritability, jet lag, mood problems, movementproblems, obsessive-compulsive disorder, pain, personality imbalances,posttraumatic stress disorder, schizophrenia and other psychoticdisorder, seasonal affective disorder, sexual disorder, sleep disorder,stuttering, substance abuse, tic disorder/Tourette's Syndrome, traumaticbrain injury, Trichotillomania, Parkinson's disease,violent/self-destructive behaviors, and any combination thereof.

The invention also encompasses a method wherein the treatment modalityis selected from the group consisting of drug therapy, electroconvulsivetherapy, electromagnetic therapy, neuromodulation therapy, talk therapy,and any combination thereof. Optionally, the treatment modality is drugtherapy and the drug is selected from the group consisting of apsychotropic agent, a neurotropic agent, a multiple of a phychotropicagent or a neurotropic agent, and any combination thereof. Optionally,the drug has a direct or indirect effect on the CNS system of thepatient. And, optionally, the drug is selected from the group consistingof alprazolam, amantadine, amitriptyline, atenolol, bethanechol,bupropion, buspirone, carbamazepine, chlorpromazine, chlordiazepoxide,citalopram, clomipramine, clonidine, clonazepam, clozapine,cyproheptadine, dexamethasone, divalproex, deprenyl, desipramine,dexamethasone, dextroamphetamine, diazepam, disulfram, divalproex,doxepin, ethchlorvynol, fluoxetine, fluvoxamine, felbamate,fluphenazine, gabapentin, haloperidol, imipramine, isocarboxazid,lamotrigine, levothyroxine, liothyronine, lithium carbonate, lithiumcitrate, lorazepam, loxapine, maprotiline, meprobamate, mesoridazine,methamphetamine, midazolam, meprobamate, mirtazapine, molindone,moclobemide, molindone, naltrexone, phenelzine, nefazodone,nortriptyline, olanzapine, oxazepam, paroxetine, pemoline, perphenazine,phenelzine, pimozide, pindolol, prazepam, propranolol, protriptyline,quetiapine, reboxetine, risperidone, selegiline, sertraline, sertindole,trifluoperazine, trimipramine, temazepam, thioridazine, topiramate,tranylcypromine, trazodone, triazolam, trihexyphenidyl, trimipramine,valproic acid, venlafaxine, and any combination thereof.

Optionally, the method for classifying physiologic brain imbalancesincludes obtaining follow-up quantified neurophysiologic information totrack physiologic changes produced by the administration of treatmentmodalities; and making therapy regime changes based on the follow-upneurophysiologic information and a patient assessment tool.

Optionally, the method for classifying physiologic brain imbalancesincludes scenarios wherein the physiologic brain imbalance accompaniespanic disorder and the treatment modality is drug therapy using a drugselected from the group consisting of valproic acid, clonazepam,carbamazepine, methylphenidate and dextroamphetamine.

Optionally, the method for classifying physiologic brain imbalancesincludes scenarios wherein the physiologic brain imbalance accompanieseating disorder and the treatment modality is drug therapy using a drugselected from the group consisting of methylphenidate anddextroamphetamine.

Optionally, the method for classifying physiologic brain imbalancesincludes scenarios wherein the physiologic brain imbalance accompanieslearning disorder and the treatment modality is drug therapy using adrug selected from the group consisting of amantadine, valproic acid,clonazepam and carbamazepine.

The invention also encompasses a method for the classification,diagnosis, and treatment of a physiologic brain imbalance of a patientat a remote location, comprising: sending the neurophysiologicinformation of the patient from the remote location to a centralprocessing location, comparing the sent information at the centralprocessing location with multivariate neurophysiologic outputmeasurements collected from a reference population of individuals toobtain a brain profile, associating at the central processing locationthe brain profile to brain profiles indicative of brain pathologies toproduce an association, and sending to the remote location a treatmentrecommendation based on the association.

The invention also encompasses a method suitable for determining theeffect of a new or known drug on the CNS system of a patient,comprising: selecting at least one patient, administering the drug tothe patient, obtaining the patient's post administration,neurophysiologic information, and analyzing the patient's postadministration, neurophysiologic information to determine the effect ofthe drug on the CNS system of the patient.

The method suitable for determining the effect of a new or known drug onthe CNS system of a patient includes scenarios wherein analyzing stepincludes comparing the patient's neurophysiologic information withneurophysiologic information obtained from a reference population ofindividuals to produce a similarities profile for the patient.Optionally, the similarities profile is used to determine the effect ofthe drug.

The method suitable for determining the effect of a new or known drug onthe CNS system of a patient includes scenarios whereinpre-administration neurophysiologic information is obtained from thepatient. Optionally, the pre-administration neurophysiologic informationis also compared to the neurophysiologic information from the referencepopulation. Optionally, the effect of the drug on the patient isdetermined by comparison of the pre and post administration sets ofneurophysiologic information from the patient.

The invention also encompasses a method for screening individualparticipants for inclusion in clinical drug trials for treatingphysiologic brain imbalances, comprising: determining whether apotential individual participant exhibits a behavioral pathology,determining whether that potential individual participant has abnormalneurophysiologic information, and establishing a set of individualparticipants from those potential individual participants exhibiting abehavioral pathology and an abnormal neurophysiologic informationassociated with the behavioral pathology.

The method for screening individual participants for inclusion inclinical drug trials for treating physiologic brain imbalances includesscenarios wherein the drug undergoing clinical testing is a new compoundor the drug undergoing clinical testing is a known compound for which anew use is indicated.

The invention also encompasses a method for treating physiologic brainimbalances, comprising: obtaining neurophysiologic information from apatient, quantifying the neurophysiologic information, and correlatingthe neurophysiologic information to therapy responsivity profiles.

Optionally, the method for treating physiologic brain imbalances furtherincludes determining from the therapy responsivity profile a treatmentof the physiologic brain imbalance of the patient.

Optionally, the method for treating physiologic brain imbalances furtherincludes scenarios wherein the neurophysiologic information is collectedusing a neurophysiologic technique selected from the group consisting ofelectroencephalograhy, magnetic resonance imaging, positron emissiontomography, single photon emission computerized tomography, and anycombination thereof. Optionally, the neurophysiologic technique iselectroencephalography. Optionally, the electroencephalography isdigitized fast Fourier transform quantitative electroencephalography.Optionally, the neurophysiologic information is stored in a database.Optionally, the correlations between neurophysiologic information andtherapy responsivity profiles are stored in a database.

The invention also encompasses a method of prescribing multipletreatments to a subject with the aid of a treatment-response database,the method comprising the steps of: obtaining neurophysiologicinformation from the subject; identifying at least one treatment optionwith the aid of the treatment-response database; selecting a firsttreatment, in response to identification of multiple treatment options,one treatment; administering the first treatment to the subject;adjusting the first treatment in accordance with an effect of thetreatment on neurophysiologic information of the subject; and selectinga second treatment in accordance with an effect of the treatment onneurophysiologic information of the subject. Optionally, the methodincludes selecting, in response to a choice between class 4 agents andother agents, a treatment including at least one of class 4 agents.Optionally, the method includes selecting, in response to a choicebetween class 2 agents and other agents, a treatment including at leastone of other agents. Optionally, the method includes Optionally, themethod includes selecting, in response to a choice between class 1agents and class 5 agents, a treatment including at least one of class 1agents. Optionally, the treatment-response database is represented by aset of rules representing cluster boundaries for identifying at leastone suitable treatment.

The invention also encompasses a method of generating a reportreflecting a prospective estimate of a response to a treatment, themethod comprising the steps of: reporting a class of an agent along withspecific agents within the class such that the specific agents areindicated for a treatment of a subject based on a neurophysiologicinformation of the subject and a treatment-response database; orderingmultiple classes in order of significance; representing responsivity toat least one treatment in the report by a responsivity code; andordering multiple agents in order of the responsivity code. Optionally,the responsivity code is color coded for easy identification.Optionally, the responsivity code includes a plurality of levelsrepresenting a range of responses in the interval defined by a positiveresponse and resistance to treatment. Optionally, the interval includesadverse responses to treatment. Optionally, the report includes aneffect of a particular treatment on neurophysiologic information of thesubject. Optionally, the report includes identification of lessexpensive treatments than a specified treatment such that the lessexpensive treatments prospectively have a substantially similar responseas the specified treatment. Optionally, the report includes orderedtreatments ordered in accordance with a cost of each of the orderedtreatments. Optionally, the report is presented via an electronicuser-interface. Optionally, the report is generated in response to anelectronic request.

The invention also encompasses a method of establishing an approved useof a therapeutic agent in treating patients having a disorder, saidagent has not heretofore been approved for treatment of said disorder inapproved clinical practice, the method comprising: indicating said agentfor treatment of said disorder where EEG information obtained from oneor more patients having said condition indicates that said agent hastherapeutic effectiveness in reference patients, whether or not thereference patients have been diagnosed with said disorder.

The invention also encompasses a method of processing data correspondingto neurophysiologic information; comprising: sending neurophysiologicinformation corresponding to one or more subjects to a processor, saidprocessor configured to i) compare said information withneurophysiologic information from a reference population to produce agroup of differences, and ii) organize said differences by outputmeasurements to provide a differences profile, so as to create processedinformation. Optionally, the method further includes receiving saidprocessed information. Optionally, the method further includes usingsaid processed information to predict the outcome of treatment of saidone or more subjects with one or more drugs prior to administering saidone or more drugs. Optionally, the method further includes using saidprocessed information in the development of a drug to generate drugdevelopment information wherein drug development information includes,unless in the contrary is indicated, any type of information required bythe FDA including data for proving safety/efficacy; labelinginformation, etc. Optionally, the method further includes submittingsaid drug development information to a government regulatory agency.Optionally, the method further includes marketing or selling a drug byassociating said differences profile with said drug, wherein the term“associating” includes direct or indirect (e.g. commercial utility)associations). Optionally, the neurophysiologic information compriseselectroencephalogram recordings recorded by electrodes placed inaccordance with the International 10/20 system. Optionally, the sendingis performed over an electronic communications network, whereinelectronic communications network includes any transmission systemincluding Internet, telephone, satellite, etc. Optionally, the sendingis performed over the Internet or over telephone or by satellitetransmission. Optionally, sending is performed at a first site and theprocessor is located at a second site, possibly with the sites indifferent countries. Optionally, receiving comprises accessing saidprocessed information from a data storage sire, wherein said datastorage site comprises a third site.

Similarly, the invention also encompasses a method of receivingprocessed information corresponding to neurophysiologic information;comprising: receiving processed neurophysiologic information from aprocessor, said processor having i) compared neurophysiologicinformation corresponding to one or more subjects with neurophysiologicinformation from a reference population to produce a group ofdifferences, and ii) organized said differences by output measurementsto provide a differences profile, so as to create processed information.

It is to be understood that the present invention also encompassesmethods for remote performance of all the prior methods along withsystems for remotely performing these prior methods (as illustrated inFIG. 15). The following embodiments are illustrative of such furthermethods and systems. In the interest of compactness without limitation,remote processing embodiments and systems corresponding to the othersuch methods and systems have been omitted.

The invention also encompasses a method for identifying a treatment fora subject based on pretreatment neurophysiologic information from thesubject and a desired outcome, the method comprising the steps of:transmitting information from a first site, the transmitted informationcomprising the pretreatment neurophysiologic information and the desiredoutcome; and receiving information at a second site, wherein thereceived information comprising an indication of at least one treatmentthat was determined by the method of claim 29 from the transmittedinformation.

Optionally, in the prior method, the information is transmitted to andreceived from a processing site performing the method of claim 29; wherethe processing site is remotely located from the first and the secondsite; or where the processing site is colocated with the first or withthe second site; or the first and the second site are colocated; or thesecond site are remotely located.

Optionally the prior method further comprises transmitting at least partof the received and at least part of the transmitted information to areviewing site; and reviewing the quality of the transmitted informationin view of the received information.

The invention also encompasses a system for identifying a treatment fora subject based on pretreatment neurophysiologic information from thesubject and a desired outcome, the method comprising: a transmittingdevice at a first site, for transmitting information comprising thepretreatment neurophysiologic information and the desired outcome; and areceiving device at a second site, for receiving information comprisingan indication of at least one treatment that was determined by themethod of claim 29 from the transmitted information.

Finally, the invention also encompasses program products comprising acomputer-readable medium having encoded instructions for causing acomputer system to perform any or all of the methods of presentinvention.

Although the preceding description of the invention is in the context ofthe embodiments described herein, the embodiments are not intended to bea limitation on the scope of the invention. As readily recognized by oneof ordinary skill in the art, the disclosed invention encompasses thedisclosed embodiments along with other embodiments providing variationson choice of indicative and univariate variables, referencedistributions, clustering strategies, software and remote treatmentimplementations and the like without departing from the form and spiritof the teaching disclosed herein.

APPENDIX I FRONTAL FRONTAL EEG EEG EEG MIDLINE MIDLINE POSTERIORABSOLUTE ABSOLUTE ABSOLUTE PROGRESSION PROGRESSION MIDLINE POWER POWERPOWER INDEX INDEX PROGRESSION AVERAGE = AVERAGE = AVERAGE = Fpz/CzFpz/Cz INDEX >300 <300 & >40 <40 (ALPHA (ALPHA Oz/Cz rEEG microvoltsmicrovolts microvolts BAND) = BAND) = (ALPHA PARAMETERS squared sq.squared >2.5 <2.5 BAND) = >1 BENZODIAZEPINE BENZODIAZEPINEBENZODIAZEPINE BETA BLOCKER BETA BLOCKER BETA BLOCKER WELLBUTRINWELLBUTRIN WELLBUTRIN WELLBUTRIN CARBAMAZEPINE CARBAMAZEPINE CLONIDINECLONIDINE CLONIDINE LITHIUM LITHIUM LITHIUM MAOI MAOI MAOI SNRI SNRISNRI SNRI SNRI SNRI SSRI SSRI SSRI SSRI SSRI SSRI STIMULANT STIMULANTSTIMULANT TCA TCA TCA TCA TCA TCA VALPROATE VALPROATE VALPROATE PROZACPROZAC PROZAC PROZAC PROZAC EFFEXOR EFFEXOR EFFEXOR EFFEXOR EFFEXORLAMICTAL LAMICTAL ADDERALL ADDERALL ADDERALL ADDERALL ADDERALL POSTERIORAVERAGE AVERAGE AVERAGE MIDLINE RATIO OF RATIO OF MIDLINE MIDLINEMIDLINE PROGRESSION FRONTAL/ FRONTAL/ (Fpz, Fz, Cz) (Fpz, Fz, Cz) (Fpz,Fz, Cz) INDEX POSTERIOR POSTERIOR THETA/ THETA/ THETA/ Oz/Cz ALPHA ALPHABETA BETA BETA rEEG (ALPHA INDICES = INDICES = RATIO = RATIO = RATIO =PARAMETERS BAND) = <1 >4 <4 >2.5 <2.5 & >1.5 <1.5 BENZODIAZEPINEBENZODIAZEPINE BENZODIAZEPINE BENZODIAZEPINE BETA BLOCKER BETA BLOCKERBETA BLOCKER BETA BLOCKER WELLBUTRIN WELLBUTRIN WELLBUTRIN WELLBUTRINCARBAMAZEPINE CARBAMAZEPINE CLONIDINE CLONIDINE CLONI- DINE LITHIUM MAOIMAOI MAOI MAOI SNRI SNRI SNRI SNRI SSRI SSRI SSRI STIMULANT STIMULANTSTIMULANT STIMULANT TCA TCA TCA TCA TCA VALPROATE VALPROATE PROZACPROZAC PROZAC PROZAC EFFEXOR EFFEXOR EFFEXOR LAMICTAL LAMICTAL ADDERALLADDERALL RMAA = rEEG RMAD = >10 +/− RMAD = <10 +/− RMAT = >10 +/− RMAT =<10 +/− RMAA = >10 +/− <10 +/− PARAMETERS RMPD = >10 RMPD = <10 RMPT= >10 RMPT = <10 RMPA = >10 RMPA = <10 BENZODIAZEPINE BENZODIAZEPINEBETA BLOCKER WELLBUTRIN WELLBUTRIN WELLBUTRIN WELLBUTRIN CARBAMAZEPINECARBAMAZEPINE CARBAMAZEPINE CLONIDINE CLONIDINE CLONIDINE CLONIDINELITHIUM MAOI MAOI MAOI MAOI SNRI SNRI SNRI SNRI SSRI SSRI SSRI SSRISTIMULANT STIMULANT STIMULANT TCA TCA TCA TCA VALPROATE VALPROATE PROZACPROZAC PROZAC PROZAC EFFEXOR EFFEXOR EFFEXOR EFFEXOR LAMICTAL LAMICTALLAMICTAL LAMICTAL ADDERALL ADDERALL ADDERALL rEEG RMAB = >10 +/− RMAB =<10 +/− CEAD = >10 +/− CEAD = <10 +/− CEAT = >10 +/− CEAT = <10 +/−PARAMETERS RMPB = >10 RMPB = <10 CEPD = >10 CEPD = <10 CEPT = >10 CEPT =<10 BENZODIAZEPINE BENZOD- BENZODI- BENZODI- IAZEPINE AZEPINE AZEPINEBETA BLOCKER BETA BLOCKER WELLBUTRIN WELLBUTRIN CARBAMAZEPINECARBAMAZEPINE CARBAMAZEPINE CLONIDINE LITHIUM LITHIUM LITHIUM LITHIUMMAOI MAOI SNRI SNRI SSRI SSRI STIMULANT STIMULANT TCA TCA VALPROATEVALPROATE VALPROATE VALPROATE PROZAC PROZAC PROZAC EFFEXOR EFFEXOREFFEXOR LAMICTAL LAMICTAL LAMICTAL LAMICTAL ADDERALL ADDERALL ADDERALLADDERALL FMAD = rEEG CEAA = >10 +/− CEAA = <10 +/− CEAB = >10 +/− CEAB =<10 +/− FMAD = >10 +/− <10 +/− PARAMETERS CEPA = >10 CEPA = <10 CEPB= >10 CEPB = <10 FMPD = >10 FMPD = <10 BENZODIAZEPINE BENZODIAZEPINEBETA BLOCKER BETA BLOCKER BETA BLOCKER WELLBUTRIN CARBAMAZEPINECARBAMAZEPINE CARBAMAZEPINE CLONIDINE LITHIUM LITHIUM LITHIUM MAOI MAOISNRI SNRI SNRI SNRI SSRI SSRI SSRI SSRI STIMULANT STIMULANT TCA TCA TCATCA VALPROATE VALPROATE PROZAC PROZAC PROZAC EFFEXOR EFFEXOR EFFEXORLAMICTAL LAMICTAL ADDERALL ADDERALL rEEG FMAT = >10 +/− FMAT = <10 +/−FMAA = >10 +/− FMAA = <10 +/− FMAB = >10 +/− FMAB = <10 +/− PARAMETERSFMPT = >10 FMPT = <10 FMPA = >10 FMPA = <10 FMPB = >10 FMPB = <10BENZODIAZEPINE BENZODI- BENZODI- BENZODI- AZEPINE AZEPINE AZEPINE BETABLOCKER BETA BLOCKER BETA BLOCKER WELLBUTRIN WELLBUTRIN WELLBUTRINWELLBUTRIN CARBAMAZEPINE CARBAMAZEPINE CARBAMAZEPINE CLONIDINE CLONIDINELITHIUM LITHIUM MAOI MAOI MAOI MAOI SNRI SNRI SNRI SSRI SSRI SSRI SSRISTIMULANT STIMULANT STIMULANT STIMULANT TCA TCA TCA TCA VALPROATEVALPROATE PROZAC PROZAC PROZAC PROZAC EFFEXOR EFFEXOR EFFEXOR EFFEXORLAMICTAL LAMICTAL LAMICTAL LAMICTAL ADDERALL ADDERALL ADDERALL ADDERALLAADL = >10, AADL = <10, AATL = >10, AATL = <10, AAAL = >10, AAAL = <10,AABL = >10, rEEG OR OR OR OR OR OR OR PARAMETERS AADR = >10 AADR = <10AATR = >10 AATR = <10 AAAR = >10 AAAR = <10 AABR = >10 BENZODIAZEPINEBETA BLOCKER WELLBUTRIN WELLBUTRIN CARBAMAZEPINE CARBAMAZEPINE CLONIDINELITHIUM MAOI MAOI MAOI MAOI SNRI SNRI SNRI SNRI SNRI SSRI SSRI SSRI SSRISSRI STIMULANT STIMULANT STIMULANT TCA TCA TCA TCA TCA VALPROATE PROZACPROZAC PROZAC EFFEXOR EFFEXOR EFFEXOR LAMICTAL ADDERALL AABL = <10, AED= >10, AET = >10, AEA = >10, AEB = >10, AEBD = >10 rEEG OR &/OR &/OR&/OR &/OR &/OR PARAMETERS AABR = <10 AED = <−10 AET = <−10 AEA = <−10AEB =<−10 AEBD = <−10 BENZODIAZEPINE BENZODIAZEPINE BENZODI-BENZODIAZEPINE BENZODIAZEPINE BENZODI- AZEPINE AZEPINE BETA BLOCKERWELLBUTRIN CARBAMAZEPINE CARBAMAZEPINE CARBAMAZEPINE CARBAMAZEPINECLONIDINE LITHIUM LITHIUM LITHIUM LITHIUM MAOI SNRI SSRI STIMULANT TCAVALPROATE VALPROATE PROZAC EFFEXOR LAMICTAL ADDERALL AEBT = >10 AEBA= >10 AEBB = >10 rEEG &/OR &/OR &/OR CADL = >10, CADR = >10, CATL = >10,CATR = >10, PARAMETERS AEBT = <−10 AEBA = <−10 AEBB = <−10 OR = <−10 OR= <−10 OR = <−10 OR = <−10 BENZODIAZEPINE BENZODI- BENZODI- BENZODI-BENZODI- BENZODI- BENZODI- BENZODIAZEPINE BETA BLOCKER AZEPINE AZEPINEAZEPINE AZEPINE AZEPINE AZEPINE WELLBUTRIN CARBAMAZEPINE CLONIDINELITHIUM LITHIUM LITHIUM LITHIUM MAOI SNRI SSRI STIMULANT TCA VALPROATEVALPROATE VALPROATE VALPROATE VALPROATE VALPROATE VALPROATE PROZACEFFEXOR LAMICTAL LAMICTAL LAMICTAL LAMICTAL LAMICTAL ADDERALL CABL =rEEG CAAL = >10, CAAR = >10, >10, CABR = >10, CEBD = >10, CEBT = >10,PARAMETERS OR = <−10 OR = <−10 OR = <−10 OR = <−10 OR = <−10 OR = <−10BENZODIAZEPINE BENZODIAZEPINE BENZODIAZEPINE BETA BLOCKER BETA BLOCKERBETA BLOCKER BETA BETA BLOCKER BLOCKER WELLBUTRIN CARBAMAZEPINECARBAMAZEPINE CARBAMAZEPINE CLONIDINE LITHIUM MAOI SNRI SSRI STIMULANTTCA VALPROATE VALPROATE VALPROATE VALPROATE PROZAC PROZAC PROZAC PROZACPROZAC EFFEXOR EFFEXOR EFFEXOR EFFEXOR EFFEXOR LAMICTAL LAMICTALLAMICTAL ADDERALL RBDL = >10, rEEG CEBA = >10, CEBB = >10, &/OR RBDL =<10, &/OR RBTL = >10, &/OR RBTL = <10, &/OR PARAMETERS OR = <−10 OR =<−10 RBDR = >10 RBDR = <10 RBTR = >10 RBTR = <10 BENZODIAZEPINE BETABLOCKER BETA BLOCKER BETA BLOCKER WELLBUTRIN WELLBUTRIN CARBAMAZEPINECARBAMAZEPINE CLONIDINE LITHIUM MAOI MAOI MAOI SNRI SNRI SNRI SSRI SSRISSRI STIMULANT STIMULANT STIMULANT TCA TCA TCA VALPROATE VALPROATEPROZAC PROZAC PROZAC PROZAC PROZAC EFFEXOR EFFEXOR EFFEXOR EFFEXOREFFEXOR LAMICTAL ADDERALL ADDERALL ADDERALL rEEG RBAL = >10, &/OR RBAL =<10, &/OR RBBL = >10, &/OR RBBL = <10, &/OR PARAMETERS RBAR = >10 RBAR =<10 RBBR = >10 RBBR = <10 BENZODIAZEPINE BETA BLOCKER WELLBUTRINCARBAMAZEPINE CLONIDINE LITHIUM LITHIUM MAOI MAOI MAOI SNRI SNRI SNRISSRI SSRI SSRI STIMULANT STIMULANT STIMULANT TCA TCA TCA VALPROATEPROZAC PROZAC PROZAC EFFEXOR EFFEXOR EFFEXOR LAMICTAL ADDERALL ADDERALLADDERALL

APPENDIX II GLU- BUPROPRION BUPROPRION TA- PHENYL- TYRO- AMITRIP- (LONG(REGULAR CITALO- GABA MINE ALANINE SINE TYLINE ACTING) TABS) BUSPIRONEPRAM 64 67 68 63 47 85 87 38 82 300.00 Anxiety Disorder NOS N N N N C NN C N 300.02 Generalized Anxiety N N N N N N C N Disorder 300.22Agoraphobia Without N N N N C N N C N History of Panic Disorder 300.23Social Phobia N N N N C N N C 300.29 Specific Phobia N N N N N N C 300.3Obsessive-Compulsive N N N N N N Disorder 309.81 Posttraumatic Stress NN N N C N N C C Disorder Panic Disorder N N N N N N C C 299.00 AutisticDisorder N N N 299.80 Pervasive N N Developmental Disorder NOS 307.20Tic Disorder NOS 307.22 Chronic Motor or N Vocal Tic Disorder 307.23Tourette's Disorder 307.9 Communication N N Disorder NOS 309.21Separation Anxiety N N N N N N N Disorder CLO- DE- FLU- FLU- MIP- SI-OX- VOX- RA- PRA- E- A- IMIPRA- MINE MINE DOXEPIN TINE MINE MINEMIRTAZAPINE MOCLOBEMIDE NEFAZODONE 42 25 24 15 69 6 77 72 54 300.00Anxiety Disorder NOS C C C C N C N N 300.02 Generalized Anxiety C C C NDisorder 300.22 Agoraphobia Without N C N N N History of Panic Disorder300.23 Social Phobia C C C N 300.29 Specific Phobia C C C N 300.3Obsessive-Compulsive C C C N Disorder 309.81 Posttraumatic Stress C C CC C N Disorder Panic Disorder C C C C C N 299.00 Autistic Disorder299.80 Pervasive N N N C N Developmental Disorder NOS 307.20 TicDisorder NOS N C 307.22 Chronic Motor or N N Vocal Tic Disorder 307.23Tourette's Disorder N 307.9 Communication Disorder NOS 309.21 SeparationAnxiety C N C N Disorder VEN- NOR- PAR- VEN- LA- CAR- TRIP- OX- TRA- LA-FAX- BA- TY- E- ZO- FAX- INE MAZE- LINE PARNATE TINE PHENALZINESELIGELINE SERTRALINE DONE INE TABLETS PINE 30 41 9 48 83 12 18 8 93 7300.00 Anxiety Disorder NOS N C N N C C N N N 300.02 Generalized AnxietyC N C C C C Disorder 300.22 Agoraphobia Without C N C N N C N N NHistory of Panic Disorder 300.23 Social Phobia C N C N N C C N N N300.29 Specific Phobia C N C N N C C N N N 300.3 Obsessive-Compulsive NN N N N N N Disorder 309.81 Posttraumatic Stress C N C N N C C N N NDisorder Panic Disorder C N C N N C C N N N 299.00 Autistic Disorder N299.80 Pervasive N N N N Developmental Disorder NOS 307.20 Tic DisorderNOS N 307.22 Chronic Motor or N Vocal Tic Disorder 307.23 Tourette'sDisorder N 307.9 Communication N N Disorder NOS 309.21 SeparationAnxiety C N C N N N N N N Disorder DIPHEN- AL- YLHY- GAB- PRA- CLO- CLO-DI- DAN- APEN- GUANFACINE CLON- ZO- NAZE- RAZE- AZE- TOIN DIVALPROEX TINLAMOTRIGINE HCL IDINE LAM PAM PATE PAM 27 2 79 76 13 17 20 23 35 57300.00 Anxiety Disorder NOS N N N C C C N 300.02 Generalized Anxiety N NN N N C C C C Disorder 300.22 Agoraphobia Without N N N C C C C Historyof Panic Disorder 300.23 Social Phobia N N N C C C C 300.29 SpecificPhobia N N N C C C C 300.3 Obsessive-Compulsive N N N N N Disorder309.81 Posttraumatic Stress N N N N C C C Disorder Panic Disorder N N NN C C C 299.00 Autistic Disorder N N N N 299.80 Pervasive N N N N N NDevelopmental Disorder NOS 307.20 Tic Disorder NOS N N N N C C N 307.22Chronic Motor or N N N N N C N N N N Vocal Tic Disorder 307.23Tourette's Disorder N N N N C C N 307.9 Communication N N N Disorder NOS309.21 Separation Anxiety N N N C C N Disorder FLU- LOR- PRO- RAZE- AZE-OXAZ- TRIA- ATEN- PRAN- LITH- PAM PAM EPAM QUAZEPAM TEMAZEPAM ZOLAM OLOLMETOPOLOL OLOL IUM 51 1 50 32 37 46 75 74 31 14 300.00 Anxiety DisorderNOS C C C N N N N 300.02 Generalized Anxiety C C C N N N Disorder 300.22Agoraphobia Without C C C N N N N History of Panic Disorder 300.23Social Phobia C C C N N N 300.29 Specific Phobia C C C 300.3Obsessive-Compulsive N N N N Disorder 309.81 Posttraumatic Stress C C CN N N N Disorder Panic Disorder C C C N N N N 299.00 Autistic Disorder NN N 299.80 Pervasive N N N N Developmental Disorder NOS 307.20 TicDisorder NOS C N N N 307.22 Chronic Motor or N N N N N N N Vocal TicDisorder 307.23 Tourette's Disorder C N N N 307.9 Communication DisorderN NOS 309.21 Separation Anxiety N N N N Disorder GINGKO BILOBA KAVA KAVASTJOHNSWORT FLUPHENAZINE HALPERIDOL LOXAPINE OLANZAPINE 84 97 73 43 34 378 300.00 Anxiety Disorder NOS N N N 300.02 Generalized Anxiety N N NDisorder 300.22 Agoraphobia Without N N N History of Panic Disorder300.23 Social Phobia N N N 300.29 Specific Phobia N N N 300.3Obsessive-Compulsive N N Disorder 309.81 Posttraumatic Stress N N NDisorder Panic Disorder N 299.00 Autistic Disorder N 299.80 Pervasive NDevelopmental Disorder NOS 307.20 Tic Disorder NOS N 307.22 ChronicMotor or N Vocal Tic Disorder 307.23 Tourette's Disorder N 307.9Communication Disorder N NOS 309.21 Separation Anxiety N N N DisorderTHIO- TRI- HY- PIM- RIDA- FLUOPER- DROX- AMAN- OZIDE RISPERDONE SEROQUELZINE THIOTHIXINE AZINE YZINE SILBTRMIN TADINE 19 33 92 16 44 28 52 80 5300.00 Anxiety Disorder NOS C N 300.02 Generalized Anxiety Disorder300.22 Agoraphobia Without N History of Panic Disorder 300.23 SocialPhobia 300.29 Specific Phobia 300.3 Obsessive-Compulsive Disorder 309.81Posttraumatic Stress N Disorder Panic Disorder N 299.00 AutisticDisorder 299.80 Pervasive Developmental Disorder NOS 307.20 Tic DisorderNOS N 307.22 Chronic Motor or C N Vocal Tic Disorder 307.23 Tourette'sDisorder 307.9 Communication Disorder NOS 309.21 Separation Anxiety NDisorder PHOTOTHERAPY, 10,000LUX ZOLIPIDEM ADDERALL DEXEDRINEMETHAMPHETAMINE METHYLPHENIDATE 86 45 70 21 71 10 300.00 AnxietyDisorder NOS N N N N N 300.02 Generalized Anxiety N Disorder 300.22Agoraphobia Without N N N N N History of Panic Disorder 300.23 SocialPhobia N N N N N 300.29 Specific Phobia N N N N 300.3Obsessive-Compulsive N N N N N Disorder 309.81 Posttraumatic Stress N NN N N Disorder Panic Disorder N N N N 299.00 Autistic Disorder N N N N299.80 Pervasive N N N N Developmental Disorder NOS 307.20 Tic DisorderNOS 307.22 Chronic Motor or N N N N Vocal Tic Disorder 307.23 Tourette'sDisorder 307.9 Communication Disorder NOS 309.21 Separation Anxiety N NN N Disorder SR MODAFINIL PEMOLINE PHENTERMINE METHYLPHENIDATE 95 22 2988 300.00 Anxiety Disorder NOS N N 300.02 Generalized Anxiety N NDisorder 300.22 Agoraphobia Without N N N History of Panic Disorder300.23 Social Phobia N N 300.29 Specific Phobia N N 300.3Obsessive-Compulsive N N Disorder 309.81 Posttraumatic Stress N NDisorder Panic Disorder N N 299.00 Autistic Disorder N 299.80 PervasiveN N N Developmental Disorder NOS 307.20 Tic Disorder NOS 307.22 ChronicMotor or N N N Vocal Tic Disorder 307.23 Tourette's Disorder 307.9Communication Disorder N N N NOS 309.21 Separation Anxiety N N NDisorder GLU- PHEN- BUPROPRION BUPROPRION TA- YLALA- TYRO- AMITRIP-(LONG (REGULAR CITAL- GABA MINE NINE SINE TYLINE ACTING) TABS) BUSPIRONEOPRAM 312.8 Conduct Disorder N N N N N 313.81 Oppositional Defiant N N NN N N N Disorder 315.9 Learning Disorder NOS N N N N N Attention- N N NN N Deficit/Hyperactivity Disorder 294.8 Amnestic Disorder N N N N N C294.9 Cognitive Disorder NOS N N N N N Dementia of the Alzheimer's N N NType 307.1 Anorexia Nervosa N N N N C N N C C 307.50 Eating Disorder NOSN N N N N N C C 307.51 Bulimia Nervosa N N N N N N C C 312.30Impulse-Control N N Disorder NOS 312.31 Pathological Gambling N N 312.34Intermittent Explosive N N N N N C N Disorder 312.39 Trichotillomania NN N N N C 296.89 Bipolar II Disorder N N N N N 296.90 Mood Disorder NOSN N N C C C C 300.4 Dysthymic Disorder C C C C C 301.13 CyclothymicDisorder N N N N N CLO- DESI- IMIP- NE- MIPRA- PRA- DOX- FLUVOX- RA-FAZO- MINE MINE EPIN FLUOXETINE AMINE MINE MIRTAZAPINE MOCLOBEMIDE DONE312.8 Conduct Disorder N C N 313.81 Oppositional Defiant C N C NDisorder 315.9 Learning Disorder NOS N N C N Attention- C N C NDeficit/Hyperactivity Disorder 294.8 Amnestic Disorder N 294.9 CognitiveDisorder NOS Dementia of the Alzheimer's Type 307.1 Anorexia Nervosa C CC C C C C N C 307.50 Eating Disorder NOS N N C C C C C N 307.51 BulimiaNervosa N N C C C C C N C 312.30 Impulse-Control C N Disorder NOS 312.31Pathological Gambling C C C N 312.34 Intermittent Explosive N C N C N NDisorder 312.39 Trichotillomania C C C N 296.89 Bipolar II Disorder N NN C N 296.90 Mood Disorder NOS C C C N C C N C 300.4 Dysthymic DisorderC C C C C C C N C 301.13 Cyclothymic Disorder N N N NOR- PAR- SER-VENLA- CARBA- TRIP- PAR- OXE- TRA- TRAZO- VENLA- FAXINE MAZE- TYLINENATE TINE PHENALZINE SELIGELINE LINE DONE FAXINE TABLETS PINE 312.8Conduct Disorder C N N N N N N N 313.81 Oppositional Defiant N N N N N NN N N Disorder 315.9 Learning Disorder NOS C N N N N N Attention- C N NN N N Deficit/Hyperactivity Disorder 294.8 Amnestic Disorder 294.9Cognitive Disorder NOS N N N N N N Dementia of the Alzheimer's Type307.1 Anorexia Nervosa C N C N N C C N N N 307.50 Eating Disorder NOS CN C N N C C N N N 307.51 Bulimia Nervosa N C N N C C N N N 312.30Impulse-Control N N Disorder NOS 312.31 Pathological Gambling N C N N CC N N N 312.34 Intermittent Explosive C N N N N N N Disorder 312.39Trichotillomania N C N N C N 296.89 Bipolar II Disorder N N N N N N N C296.90 Mood Disorder NOS C N C N N C C C C N 300.4 Dysthymic Disorder CN C N N C C C C N 301.13 Cyclothymic Disorder N N N N N N N DIPHEN- AL-YLHY- GAB- PRA- CLO- CLO- DI- DAN- APEN- GUANFACINE CLON- ZO- NAZE-RAZE- AZE- TOIN DIVALPROEX TIN LAMOTRIGINE HCL IDINE LAM PAM PATE PAM312.8 Conduct Disorder N N N N N 313.81 Oppositional Defiant N N N N N NDisorder 315.9 Learning Disorder NOS N N Attention- N N N C CDeficit/Hyperactivity Disorder 294.8 Amnestic Disorder C C C 294.9Cognitive Disorder N N NOS Dementia of the Alzheimer's N Type 307.1Anorexia Nervosa N N N C C C N 307.50 Eating Disorder NOS N N N N C C NN 307.51 Bulimia Nervosa N N N N N N 312.30 Impulse-Control N N N N N NC C N Disorder NOS 312.31 Pathological Gambling N N N N N N N 312.34Intermittent Explosive N N N N N N C C C Disorder 312.39Trichotillomania N N N C C C 296.89 Bipolar II Disorder N C C N C C N296.90 Mood Disorder NOS N N N N C C C 300.4 Dysthymic Disorder N N N NN N 301.13 Cyclothymic Disorder N N N N N C C C N FLU- LOR- PRO- RAZE-AZE- OXAZ- TRIA- ATEN- PRAN- LITH- PAM PAM EPAM QUAZEPAM TEMAZEPAM ZOLAMOLOL METOPOLOL OLOL IUM 312.8 Conduct Disorder N N N N 313.81Oppositional Defiant N N N N Disorder 315.9 Learning Disorder NOS N N NAttention- N N N Deficit/Hyperactivity Disorder 294.8 Amnestic DisorderC 294.9 Cognitive Disorder NOS N N N N Dementia of the Alzheimer's Type307.1 Anorexia Nervosa C N N N N 307.50 Eating Disorder NOS N N N N307.51 Bulimia Nervosa N N N N N 312.30 Impulse-Control C N N N NDisorder NOS 312.31 Pathological Gambling N N N N C 312.34 IntermittentExplosive C C N N N N Disorder 312.39 Trichotillomania N N N 296.89Bipolar II Disorder N N N N C 296.90 Mood Disorder NOS N N N N 300.4Dysthymic Disorder N N 301.13 Cyclothymic Disorder N N N C GINGKO BILOBAKAVA KAVA STJOHNSWORT FLUPHENAZINE HALPERIDOL LOXAPINE OLANZAPINE 312.8Conduct Disorder N 313.81 Oppositional Defiant N N N Disorder 315.9Learning Disorder NOS N Attention- N Deficit/Hyperactivity Disorder294.8 Amnestic Disorder N 294.9 Cognitive Disorder NOS N Dementia of theAlzheimer's N Type 307.1 Anorexia Nervosa N N N 307.50 Eating DisorderNOS N N C 307.51 Bulimia Nervosa N N N C 312.30 Impulse-Control DisorderNOS 312.31 Pathological Gambling 312.34 Intermittent Explosive N C C C CDisorder 312.39 Trichotillomania N 296.89 Bipolar II Disorder N N C C C296.90 Mood Disorder NOS N 300.4 Dysthymic Disorder N 301.13 CyclothymicDisorder THIO- TRI- HY- PIM- RIDA- FLUOPER- DROX- AMAN- OZIDE RISPERDONESEROQUEL ZINE THIOTHIXINE AZINE YZINE SILBTRMIN TADINE 312.8 ConductDisorder N 313.81 Oppositional Defiant N Disorder 315.9 LearningDisorder NOS N Attention- N Deficit/Hyperactivity Disorder 294.8Amnestic Disorder 294.9 Cognitive Disorder NOS Dementia of theAlzheimer's Type 307.1 Anorexia Nervosa C 307.50 Eating Disorder NOS307.51 Bulimia Nervosa C C 312.30 Impulse-Control N Disorder NOS 312.31Pathological Gambling 312.34 Intermittent Explosive C C C C C N NDisorder 312.39 Trichotillomania 296.89 Bipolar II Disorder C 296.90Mood Disorder NOS 300.4 Dysthymic Disorder 301.13 Cyclothymic DisorderPHOTOTHERAPY, 10,000 LUX ZOLIPIDEM ADDERALL DEXEDRINE METHAMPHETAMINEMETHYLPHENIDATE 312.8 Conduct Disorder N N N N 313.81 OppositionalDefiant N N N N N Disorder 315.9 Learning Disorder NOS N N N NAttention- C C C C Deficit/Hyperactivity Disorder 294.8 AmnesticDisorder N 294.9 Cognitive Disorder N N N N NOS Dementia of theAlzheimer's Type 307.1 Anorexia Nervosa N N N N N 307.50 Eating DisorderNOS N N N N 307.51 Bulimia Nervosa N N N N N 312.30 Impulse-Control N NN N Disorder NOS 312.31 Pathological Gambling N N N N 312.34Intermittent Explosive N N N N Disorder 312.39 Trichotillomania N N N NN 296.89 Bipolar II Disorder N N N N N 296.90 Mood Disorder NOS N N N NN 300.4 Dysthymic Disorder N N N N N 301.13 Cyclothymic Disorder N N N NSR MODAFINIL PEMOLINE PHENTERMINE METHYLPHENIDATE 312.8 Conduct DisorderN N N 313.81 Oppositional Defiant N N Disorder 315.9 Learning DisorderNOS N N Attention- C Deficit/Hyperactivity Disorder 294.8 AmnesticDisorder N N N 294.9 Cognitive Disorder NOS N N N Dementia of theAlzheimer's N Type 307.1 Anorexia Nervosa N N 307.50 Eating Disorder NOSN N N 307.51 Bulimia Nervosa N N 312.30 Impulse-Control N N Disorder NOS312.31 Pathological Gambling N N 312.34 Intermittent Explosive N NDisorder 312.39 Trichotillomania N 296.89 Bipolar II Disorder N N 296.90Mood Disorder NOS N N 300.4 Dysthymic Disorder N N N 301.13 CyclothymicDisorder N N BUPROPRION BUPROPRION GLUTA- PHENYL- AMITRIP- (LONG(REGULAR BUSPI- CITALO- GABA MINE ALANINE TYROSINE TYLINE ACTING) TABS)RONE PRAM 311 Depressive Disorder NOS N N N N N Bipolar I Disorder N NMajor Depressive Disorder, N N N C C C C Recurrent Major DepressiveDisorder, N N N C C C C Single Episode 316 Psychological Factors C C NAffecting Medical Condition, Irritable Bowel Syndrome 316 PsychologicalFactors N N N N N Affecting Medical Condition, Atypical Asthma 316Psychological Factors Affecting Medical Condition, Hypertensive DisorderNOS 316 Psychological Factors N C N N C N Affecting Medical Condition,Neurodermatitis 301.20 Schizoid Personality N N N N N Disorder 301.22Schizotypal N N N N N Personality Disorder 301.4 Obsessive-Compulsive NN N N N N Personality Disorder 301.50 Histrionic Personality N N N N N CDisorder 301.6 Dependent Personality N N N N N Disorder 301.7 AntisocialPersonality N N N N N N Disorder 301.82 Avoidant Personality N N N N N NDisorder 301.83 Borderline Personality N N N N N N Disorder 302.71Hypoactive Sexual N N Desire Disorder 307.42 Primary Insomnia N C N N NN DESI- CLOMIP- PRA- DOX- FLUOX- FLUVOX- IMIP- NEFAZO- RAMINE MINE EPINETINE AMINE RAMINE MIRTAZAPINE MOCLOBEMIDE DONE 311 Depressive DisorderNOS C N C C N C Bipolar I Disorder N N N N Major Depressive Disorder, CC C N C C N C Recurrent Major Depressive Disorder, C C C N C C N CSingle Episode 316 Psychological Factors N C C C N Affecting MedicalCondition, Irritable Bowel Syndrome 316 Psychological Factors NAffecting Medical Condition, Atypical Asthma 316 Psychological Factors CN N Affecting Medical Condition, Hypertensive Disorder NOS 316Psychological Factors N C C N N N N Affecting Medical Condition,Neurodermatitis 301.20 Schizoid Personality C N N Disorder 301.22Schizotypal C Personality Disorder 301.4 Obsessive-Compulsive C C C NPersonality Disorder 301.50 Histrionic Personality C N Disorder 301.6Dependent Personality N N N Disorder 301.7 Antisocial Personality C C CN Disorder 301.82 Avoidant Personality N N C N Disorder 301.83Borderline Personality N C C N Disorder 302.71 Hypoactive N SexualDesire 307.42 Primary Insomnia N C N N C N NOR- PAR- SER- TRA- VENLA-CARBA- TRIP- OXE- TRA- ZO- VENLA- FAXINE MAZE- TYLINE PARNATE TINEPHENALZINE SELIGELINE LINE DONE FAXINE TABLETS PINE 311 DepressiveDisorder NOS N N N N N N Bipolar I Disorder N N N N N N N C MajorDepressive Disorder, C N C N N C C C C N Recurrent Major DepressiveDisorder, C N C N N C C C C N Single Episode 316 Psychological Factors CC N N N Affecting Medical Condition, Irritable Bowel Syndrome 316Psychological Factors N N N N N N Affecting Medical Condition, AtypicalAsthma 316 Psychological Factors Affecting Medical Condition,Hypertensive Disorder NOS 316 Psychological Factors N N N N N N N N NAffecting Medical Condition, Neurodermatitis 301.20 Schizoid PersonalityN N N N N N N Disorder 301.22 Schizotypal Personality Disorder 301.4Obsessive-Compulsive N N N N N N N Personality Disorder 301.50Histrionic Personality C N C N N N Disorder 301.6 Dependent PersonalityN N N N N N N Disorder 301.7 Antisocial Personality N N N N N N Disorder301.82 Avoidant Personality N N N N N N Disorder 301.83 BorderlinePersonality N N N C N N N Disorder 302.71 Hypoactive Sexual N N N Desire307.42 Primary Insomnia N N N N N N N DI- PHEN- GAB- AL- YLHY- A- PRA-CLO- CLO- DIAZ- DAN- PEN- GUANFACINE CLONI- ZO- NAZE- RAZE- E- TOINDIVALPROEX TIN LAMOTRIGINE HCL DINE LAM PAM PATE PAM 311 DepressiveDisorder NOS N N N N N N N N N N Bipolar I Disorder N C C N C C MajorDepressive Disorder, N N N N C C C Recurrent Major Depressive Disorder,N N N N C C C Single Episode 316 Psychological Factors N N N C C C CAffecting Medical Condition, Irritable Bowel Syndrome 316 PsychologicalFactors N N N Affecting Medical Condition, Atypical Asthma 316Psychological Factors N N Affecting Medical Condition, HypertensiveDisorder NOS 316 Psychological Factors N N N N N C C C C AffectingMedical Condition, Neurodermatitis 301.20 Schizoid Personality N N N C CDisorder 301.22 Schizotypal Personality Disorder 301.4Obsessive-Compulsive N N N N N Personality Disorder 301.50 HistrionicPersonality N N N C C Disorder 301.6 Dependent Personality N N N N N N NDisorder 301.7 Antisocial Personality N N N N C C C Disorder 301.82Avoidant Personality N N N C C Disorder 301.83 Borderline Personality NN N N C N N Disorder 302.71 Hypoactive Sexual Desire 307.42 PrimaryInsomnia N N N N N C C C FLURAZ- LORAZ- OXAZ- QUA- TEMAZ- TRIA- ATEN-PROPRAN- EPAM EPAM EPAM ZEPAM EPAM ZOLAM OLOL METOPOLOL OLOL LITHIUM 311Depressive Disorder NOS N N N N Bipolar I Disorder N N N N C MajorDepressive Disorder, N N N N Recurrent Major Depressive Disorder, N N NN Single Episode 316 Psychological Factors C C N N N Affecting MedicalCondition, Irritable Bowel Syndrome 316 Psychological Factors AffectingMedical Condition, Atypical Asthma 316 Psychological Factors N N NAffecting Medical Condition, Hypertensive Disorder NOS 316 PsychologicalFactors C C C N N N Affecting Medical Condition, Neurodermatitis 301.20Schizoid Personality C C N N N N Disorder 301.22 Schizotypal N N N NPersonality Disorder 301.4 Obsessive-Compulsive N N N N PersonalityDisorder 301.50 Histrionic Personality N N N N Disorder 301.6 DependentPersonality N N N Disorder 301.7 Antisocial Personality N N N N Disorder301.82 Avoidant Personality N N N Disorder 301.83 Borderline PersonalityN N N N Disorder 302.71 Hypoactive Sexual Desire 307.42 Primary InsomniaC C C C C C N N N N GINGKO BILOBA KAVA KAVA STJOHNSWORT FLUPHENAZINEHALPERIDOL LOXAPINE OLANZAPINE 311 Depressive Disorder NOS N N Bipolar IDisorder N N C C C Major Depressive Disorder, N Recurrent MajorDepressive Disorder, N Single Episode 316 Psychological Factors N NAffecting Medical Condition, Irritable Bowel Syndrome 316 PsychologicalFactors N N Affecting Medical Condition, Atypical Asthma 316Psychological Factors Affecting Medical Condition, Hypertensive DisorderNOS 316 Psychological Factors N N N Affecting Medical Condition,Neurodermatitis 301.20 Schizoid Personality N N N C C C Disorder 301.22Schizotypal Personality N C C C Disorder 301.4 Obsessive-Compulsive N NPersonality Disorder 301.50 Histrionic Personality Disorder 301.6Dependent Personality N Disorder 301.7 Antisocial Personality N Disorder301.82 Avoidant Personality N N N Disorder 301.83 Borderline PersonalityN N C C C Disorder 302.71 Hypoactive Sexual Desire 307.42 PrimaryInsomnia N N THIO- HY- PIMO- RIDA- TRIFLUO- DROX- AMAN- ZIDE RISPERDONESEROQUEL ZINE THIOTHIXINE PERAZINE YZINE SILBTRMIN TADINE 311 DepressiveDisorder NOS Bipolar I Disorder C Major Depressive Disorder, RecurrentMajor Depressive Disorder, Single Episode 316 Psychological FactorsAffecting Medical Condition, Irritable Bowel Syndrome 316 PsychologicalFactors Affecting Medical Condition, Atypical Asthma 316 PsychologicalFactors Affecting Medical Condition, Hypertensive Disorder NOS 316Psychological Factors N Affecting Medical Condition, Neurodermatitis301.20 Schizoid Personality C C Disorder 301.22 Schizotypal C CPersonality Disorder 301.4 Obsessive-Compulsive Personality Disorder301.50 Histrionic Personality Disorder 301.6 Dependent PersonalityDisorder 301.7 Antisocial Personality C Disorder 301.82 AvoidantPersonality Disorder 301.83 Borderline Personality C C C Disorder 302.71Hypoactive Sexual Desire 307.42 Primary Insomnia C PHOTOTHERAPY, 10,000LUX ZOLIPIDEM ADDERALL DEXEDRINE METHAMPHETAMINE METHYLPHENIDATE 311Depressive Disorder NOS N N N N N Bipolar I Disorder N N N N N MajorDepressive Disorder, N N N N N Recurrent Major Depressive Disorder, N NN N N Single Episode 316 Psychological Factors Affecting MedicalCondition, Irritable Bowel Syndrome 316 Psychological Factors N N N N NAffecting Medical Condition, Atypical Asthma 316 Psychological Factors NN Affecting Medical Condition, Hypertensive Disorder NOS 316Psychological Factors N N N N N Affecting Medical Condition,Neurodermatitis 301.20 Schizoid Personality N N N N N Disorder 301.22Schizotypal Personality Disorder 301.4 Obsessive-Compulsive N N N N NPersonality Disorder 301.50 Histrionic Personality N N N Disorder 301.6Dependent Personality N N Disorder 301.7 Antisocial Personality N N N NN Disorder 301.82 Avoidant Personality N N N N N Disorder 301.83Borderline Personality N N N N N Disorder 302.71 Hypoactive SexualDesire 307.42 Primary Insomnia N C N N N N SR MODAFINIL PEMOLINEPHENTERMINE METHYLPHENIDATE 311 Depressive Disorder NOS N N N Bipolar IDisorder N N Major Depressive Disorder, N N Recurrent Major DepressiveDisorder, N N Single Episode 316 Psychological Factors N AffectingMedical Condition, Irritable Bowel Syndrome 316 Psychological Factors NN N Affecting Medical Condition, Atypical Asthma 316 PsychologicalFactors Affecting Medical Condition, Hypertensive Disorder NOS 316Psychological Factors N Affecting Medical Condition, Neurodermatitis301.20 Schizoid Personality N N Disorder 301.22 Schizotypal PersonalityN Disorder 301.4 Obsessive-Compulsive N N Personality Disorder 301.50Histrionic Personality N N Disorder 301.6 Dependent Personality N N NDisorder 301.7 Antisocial Personality N N Disorder 301.82 AvoidantPersonality N Disorder 301.83 Borderline Personality N N Disorder 302.71Hypoactive Sexual Desire 307.42 Primary Insomnia N N GLU- PHEN-BUPROPRION BUPROPRION TA- YLALA- TYRO- AMITRIP- (LONG (REGULAR CITAL-GABA MINE NINE SINE TYLINE ACTING) TABS) BUSPIRONE OPRAM 307.44Hypersomnia related N N N N N to . . . [Indicate the Axis I or Axis IIDisorder] 307.44 Primary Hypersomnia N N N N N 307.45 Circadian RhythmSleep N C N N Disorder 307.47 Dyssomnia NOS N C N N N N 307.47Parasomnia NOS C N N N 780.59 Breathing-Related Sleep N N N N N Disorder300.7 Body Dysmorphic N N N N N N Disorder 300.7 Hypochondriasis N N N NN N 300.81 Somatization Disorder N N N N C N N C 300.81 SomatoformDisorder N N N N N N C NOS Pain Disorder 307.89 Pain C N N N AssociatedWith Both Psychological Factors and a General Medical Condition AlcoholAbuse & Dependence N N N N C N N C C Amphetamine Abuse & N N N C N N C CDependence Cannabis Abuse & N N N N N N Dependence Cocaine Abuse & N N NN N Dependence Inhalant Abuse & Dependence Nicotine Dependence N N N C COpioid Abuse & Dependence N N Sedative, Hypnotic or N N N N C N N NAnxiolytic Abuse & Dependence CLO- DESI- IMIP- NE- MIPRA- PRA- DOX-FLUVOX- RA- FAZO- MINE MINE EPIN FLUOXETINE AMINE MINE MIRTAZAPINEMOCLOBEMIDE DONE 307.44 Hypersomnia related N to . . . [Indicate theAxis I or Axis II Disorder] 307.44 Primary Hypersomnia N 307.45Circadian Rhythm C N N Sleep Disorder 307.47 Dyssomnia NOS C N C N307.47 Parasomnia NOS N C N 780.59 Breathing-Related N N Sleep Disorder300.7 Body Dysmorphic C C N N Disorder 300.7 Hypochondriasis C C N N300.81 Somatization Disorder C C C N 300.81 Somatoform Disorder C C C NNOS Pain Disorder 307.89 Pain C C C C N Associated With BothPsychological Factors and a General Medical Condition Alcohol Abuse &Dependence C C C N N C N C Amphetamine Abuse & C C C C N DependenceCannabis Abuse & N N N N Dependence Cocaine Abuse & Dependence InhalantAbuse & Dependence Nicotine Dependence N Opioid Abuse & Dependence NSedative, Hypnotic or C C C C N C N Anxiolytic Abuse & Dependence NOR-PAR- SER- VENLA- CARBA- TRIP- PAR- OXE- TRA- TRAZO- VENLA- FAXINE MAZE-TYLINE NATE TINE PHENALZINE SELIGELINE LINE DONE FAXINE TABLETS PINE307.44 Hypersomnia related C N N N to . . . [Indicate the Axis I or AxisII Disorder] 307.44 Primary Hypersomnia N N N N N 307.45 CircadianRhythm N N N N Sleep Disorder 307.47 Dyssomnia NOS N N N C N N N 307.47Parasomnia NOS N N N 780.59 Breathing-Related N Sleep Disorder 300.7Body Dysmorphic N C N N C N N N Disorder 300.7 Hypochondriasis C N C N NC C N 300.81 Somatization Disorder C N C N N C C N N N 300.81 SomatoformDisorder C N C N N C C N N N NOS Pain Disorder 307.89 Pain N C N N C N NN Associated With Both Psychological Factors and a General MedicalCondition Alcohol Abuse & Dependence N C N N C N N N Amphetamine Abuse &N N N N N N Dependence Cannabis Abuse & N N N N Dependence Cocaine Abuse& N N N N N N Dependence Inhalant Abuse & N Dependence NicotineDependence Opioid Abuse & Dependence N N N N Sedative, Hypnotic or N C NN C C N Anxiolytic Abuse & Dependence DIPHEN- AL- YLHY- GAB- PRA- CLO-CLO- DI- DAN- APEN- GUANFACINE CLON- ZO- NAZE- RAZE- AZE- TOINDIVALPROEX TIN LAMOTRIGINE HCL IDINE LAM PAM PATE PAM 307.44 Hypersomniarelated to . . . [Indicate the Axis I or Axis II Disorder] 307.44Primary Hypersomnia 307.45 Circadian Rhythm N N N N N N C C C SleepDisorder 307.47 Dyssomnia NOS N N N N N N C C N C 307.47 Parasomnia NOSN N N C 780.59 Breathing-Related Sleep Disorder 300.7 Body Dysmorphic NN N N N Disorder 300.7 Hypochondriasis N N N C C N 300.81 SomatizationDisorder N N N C C C C 300.81 Somatoform Disorder N N N C C C C NOS PainDisorder 307.89 Pain N N N N N N N Associated With Both PsychologicalFactors and a General Medical Condition Alcohol Abuse & Dependence N N NN C C C C C Amphetamine Abuse & N N N C C C C Dependence Cannabis Abuse& N N N N N N N N N N Dependence Cocaine Abuse & N N N N C C NDependence Inhalant Abuse & N N N N N Dependence Nicotine Dependence N NN Opioid Abuse & Dependence N N N Sedative, Hypnotic or N N N N C C CAnxiolytic Abuse & Dependence FLU- LOR- PRO- RAZE- AZE- OXAZ- TRIA-ATEN- PRAN- LITH- PAM PAM EPAM QUAZEPAM TEMAZEPAM ZOLAM OLOL METOPOLOLOLOL IUM 307.44 Hypersomnia related to . . . [Indicate the Axis I orAxis II Disorder] 307.44 Primary Hypersomnia 307.45 Circadian RhythmSleep C C C C C C N N N N Disorder 307.47 Dyssomnia NOS C C C C C C N NN N 307.47 Parasomnia NOS C C C 780.59 Breathing-Related Sleep Disorder300.7 Body Dysmorphic N N N N Disorder 300.7 Hypochondriasis N N N300.81 Somatization Disorder C C C N N N 300.81 Somatoform Disorder C CC N N N NOS Pain Disorder 307.89 Pain N N N N N N Associated With BothPsychological Factors and a General Medical Condition Alcohol Abuse &Dependence C C C C N N N C Amphetamine Abuse & C C C N N N DependenceCannabis Abuse & N N N N N N N Dependence Cocaine Abuse & N DependenceInhalant Abuse & N N N N Dependence Nicotine Dependence N N N OpioidAbuse & Dependence N N N Sedative, Hypnotic or C N N N N AnxiolyticAbuse & Dependence GINGKO BILOBA KAVA KAVA STJOHNSWORT FLUPHENAZINEHALPERIDOL LOXAPINE OLANZAPINE 307.44 Hypersomnia related to . . .[Indicate the Axis I or Axis II Disorder] 307.44 Primary Hypersomnia N307.45 Circadian Rhythm Sleep N Disorder 307.47 Dyssomnia NOS N N 307.47Parasomnia NOS 780.59 Breathing-Related Sleep N Disorder 300.7 BodyDysmorphic N N C C Disorder 300.7 Hypochondriasis N 300.81 SomatizationDisorder N N N 300.81 Somatoform Disorder N N N NOS Pain Disorder 307.89Pain N N Associated With Both Psychological Factors and a GeneralMedical Condition Alcohol Abuse & Dependence N N Amphetamine Abuse & N NDependence Cannabis Abuse & N Dependence Cocaine Abuse & N DependenceInhalant Abuse & Dependence Nicotine Dependence Opioid Abuse &Dependence Sedative, Hypnotic or N N N Anxiolytic Abuse & DependenceTHIO- TRI- HY- PIM- RIDA- FLUOPER- DROX- AMAN- OZIDE RISPERDONE SEROQUELZINE THIOTHIXINE AZINE YZINE SILBTRMIN TADINE 307.44 Hypersomnia relatedto . . . [Indicate the Axis I or Axis II Disorder] 307.44 PrimaryHypersomnia 307.45 Circadian Rhythm C N Sleep Disorder 307.47 DyssomniaNOS C 307.47 Parasomnia NOS C 780.59 Breathing-Related Sleep Disorder300.7 Body Dysmorphic Disorder 300.7 Hypochondriasis 300.81 SomatizationDisorder 300.81 Somatoform Disorder NOS Pain Disorder 307.89 Pain NAssociated With Both Psychological Factors and a General MedicalCondition Alcohol Abuse & Dependence Amphetamine Abuse & C N DependenceCannabis Abuse & N Dependence Cocaine Abuse & C Dependence InhalantAbuse & N Dependence Nicotine Dependence Opioid Abuse & DependenceSedative, Hypnotic or N Anxiolytic Abuse & Dependence PHOTOTHERAPY,10,000 LUX ZOLIPIDEM ADDERALL DEXEDRINE METHAMPHETAMINE METHYLPHENIDATE307.44 Hypersomnia related N N N N to . . . [Indicate the Axis I or AxisII Disorder] 307.44 Primary Hypersomnia N N N N N 307.45 CircadianRhythm N C N N N N Sleep Disorder 307.47 Dyssomnia NOS N C N N N 307.47Parasomnia NOS N C N N N N 780.59 Breathing-Related N N N N SleepDisorder 300.7 Body Dysmorphic N N N N N Disorder 300.7 HypochondriasisN N N N 300.81 Somatization Disorder N N N N N 300.81 SomatoformDisorder N N N N N NOS Pain Disorder 307.89 Pain N N N N N AssociatedWith Both Psychological Factors and a General Medical Condition AlcoholAbuse & Dependence N C N N N N Amphetamine Abuse & N C N N N NDependence Cannabis Abuse & N N N N Dependence Cocaine Abuse & N N N N NDependence Inhalant Abuse & Dependence Nicotine Dependence N N N NOpioid Abuse & Dependence N N N N N Sedative, Hypnotic or N N N N NAnxiolytic Abuse & Dependence SR MODAFINIL PEMOLINE PHENTERMINEMETHYLPHENIDATE 307.44 Hypersomnia related C N to . . . [Indicate theAxis I or Axis II Disorder] 307.44 Primary Hypersomnia N N N 307.45Circadian Rhythm Sleep N N N Disorder 307.47 Dyssomnia NOS N N 307.47Parasomnia NOS N N 780.59 Breathing-Related Sleep N N N Disorder 300.7Body Dysmorphic N N Disorder 300.7 Hypochondriasis N N 300.81Somatization Disorder N N 300.81 Somatoform Disorder N N NOS PainDisorder 307.89 Pain N N Associated With Both Psychological Factors anda General Medical Condition Alcohol Abuse & Dependence N N AmphetamineAbuse & N N Dependence Cannabis Abuse & N N N Dependence Cocaine Abuse &N N N Dependence Inhalant Abuse & N N Dependence Nicotine Dependence N NOpioid Abuse & Dependence N Sedative, Hypnotic or N C N Anxiolytic Abuse& Dependence CODE: C = COMMON USE; N = NOVEL USE FROM rEEG FEATURES; N/?& ? = POSSIBLE USE FROM rEEG FEATURES

1-62. (canceled)
 63. A neurometric database, comprising: a) aggregateneurophysiologic information related to a symptomatic referencepopulation, said population comprising a plurality of subjects; and b)at least one score related to clinical manifestations from at least oneof said plurality of subjects having been diagnosed with at least onepsychiatric imbalance.
 64. The database of claim 63, wherein saidinformation comprises a plurality of quantitative univariate measures.65. The database of claim 63, wherein said reference populationcomprises at least one of said plurality of subjects having undergone atherapy regimen.
 66. The database of claim 63, wherein said referencepopulation comprises at least one of said plurality of subjects havingnot undergone a therapy regimen.
 67. The database of claim 65, whereinsaid therapy regimen is selected from the group consisting of drugtherapy, electroconvulsive therapy, electromagnetic therapy,neuromodulation therapy, and verbal therapy.
 68. The database of claim66, wherein said therapy regimen is selected from the group consistingof drug therapy, electroconvulsive therapy, electromagnetic therapy,neuromodulation therapy, and verbal therapy.
 69. The database of claim63, wherein said at least one psychiatric imbalance is selected from thegroup consisting of agitation, attention deficit hyperactivity disorder,abuse, Alzheimer's disease/dementia, anxiety, panic, and phobicdisorders, bipolar disorders, borderline personality disorder, behaviorcontrol problems, body dysmorphic disorder, cognitive problems,depression, dissociative disorders, eating, appetite, and weightproblems, edema, fatigue, hiccups, impulse-control problems,irritability, mood problems, movement problems, obsessive-compulsivedisorder, pain, personality disorders, posttraumatic stress disorder,schizophrenia and other psychotic disorders, seasonal affectivedisorder, sexual disorders, sleep disorders, stuttering, substanceabuse, tic disorders/Tourette's Syndrome, traumatic brain injury,Trichotillomania, and violent/self-destructive behaviors.
 70. Thedatabase of claim 68, wherein said database further comprises amedication response profile for said therapy regimen.
 71. The databaseof claim 70, wherein said profile comprises at least one multivariate Zscore.
 72. The database of claim 71, wherein said at least onemultivariate Z score comprises at least one multivariate outcomesmeasure.
 73. The database of claim 68, wherein said drug therapy isselected from the group consisting of alprazolam, amantadine,amitriptyline, atenolol, bethanechol, bupropion, buspirone,carbamazepine, chlorpromazine, chlordiazepoxide, citalopram,clomipramine, clonidine, clonazepam, clozapine, cyproheptadine,divalproex, deprenyl, desipramine, dextroamphetamine, diazepam,disulfiram, divalproex, doxepin, ethchlorvynol, fluoxetine, fluvoxamine,felbamate, fluphenazine, gabapentin, haloperidol, imipramine,isocarboxazid, lamotrigine, levothyroxine, liothyronine, lithiumcarbonate, lithium citrate, lorazepam, loxapine, maprotiline,meprobamate, mesoridazine, methamphetamine, midazolam, meprobamate,mirtazapine, molindone, moclobemide, naltrexone, phenelzine, nefazodone,nortriptyline, olanzapine, oxazepam, paroxetine, pemoline, perphenazine,phenelzine, pimozide, pindolol, prazepam, propranolol, protriptyline,quetiapine, reboxetine, risperidone, selegiline, sertraline, sertindole,trifluoperazine, trimipramine, temazepam, thioridazine, topiramate,tranylcypromine, trazodone, triazolam, trihexyphenidyl, trimipramine,valproic acid or venlafaxine.
 74. The database of claim 63, wherein saidscore comprises a Clinical Global Improvement score.
 75. The database ofclaim 63, wherein said Clinical Global Improvement (CGI) score isselected from the group consisting of a CGI score of negative one (−1)indicating an adverse medication effect; a CGI score of zero (0)indicating no improvement; a CGI score of one (1) indicating minimal ormild improvement; a CGI score of two 2 indicating moderate improvement;and a CGI score of three (3) indicating marked improvement, includingcomplete absence of symptoms.
 76. A method of creating a neurometricdatabase, comprising: a) providing: i) aggregate neurophysiologicinformation related to a symptomatic reference population, saidpopulation comprising a plurality of subjects; and ii) at least onescore related to clinical manifestations from at least one of saidplurality of subjects having been diagnosed with at least onepsychiatric imbalance; iii) a storage medium, wherein said informationand said manifestation scores are capable of linked retrieval andobservation; b) storing said information and said manifestations on saidmedium under conditions such that said linked information andmanifestations may be compared to a patient neurophysiologicalinformation set.
 77. The method of claim 76, wherein said informationcomprises a plurality of quantitative univariate measures.
 78. Themethod of claim 76, wherein said reference population comprises at leastone of said plurality of subjects having undergone a therapy regimen.79. The method of claim 78, wherein said reference population comprisesat least one of said plurality of subjects having not undergone atherapy regimen.
 80. The method of claim 79, wherein said therapyregimen is selected from the group consisting of drug therapy,electroconvulsive therapy, electromagnetic therapy, neuromodulationtherapy, and verbal therapy.
 81. The method of claim 79, wherein saidtherapy regimen is selected from the group consisting of drug therapy,electroconvulsive therapy, electromagnetic therapy, neuromodulationtherapy, and verbal therapy.
 82. The method of claim 76, wherein said atleast one psychiatric imbalance is selected from the group consisting ofagitation, attention deficit hyperactivity disorder, abuse, Alzheimer'sdisease/dementia, anxiety, panic, and phobic disorders, bipolardisorders, borderline personality disorder, behavior control problems,body dysmorphic disorder, cognitive problems, depression, dissociativedisorders, eating, appetite, and weight problems, edema, fatigue,hiccups, impulse-control problems, irritability, mood problems, movementproblems, obsessive-compulsive disorder, pain, personality disorders,posttraumatic stress disorder, schizophrenia and other psychoticdisorders, seasonal affective disorder, sexual disorders, sleepdisorders, stuttering, substance abuse, tic disorders/Tourette'sSyndrome, traumatic brain injury, Trichotillomania, andviolent/self-destructive behaviors.
 83. The method of claim 76, whereinsaid database further comprises a medication response profile for saidtherapy regimen.
 84. The method of claim 83, wherein said profilecomprises at least one multivariate Z score.
 85. The method of claim 84,wherein said at least one multivariate Z score comprises at least onemultivariate outcomes measure.
 86. The method of claim 81, wherein saiddrug therapy is selected from the group consisting of alprazolam,amantadine, amitriptyline, atenolol, bethanechol, bupropion, buspirone,carbamazepine, chlorpromazine, chlordiazepoxide, citalopram,clomipramine, clonidine, clonazepam, clozapine, cyproheptadine,divalproex, deprenyl, desipramine, dextroamphetamine, diazepam,disulfiram, divalproex, doxepin, ethchlorvynol, fluoxetine, fluvoxamine,felbamate, fluphenazine, gabapentin, haloperidol, imipramine,isocarboxazid, lamotrigine, levothyroxine, liothyronine, lithiumcarbonate, lithium citrate, lorazepam, loxapine, maprotiline,meprobamate, mesoridazine, methamphetamine, midazolam, meprobamate,mirtazapine, molindone, moclobemide, naltrexone, phenelzine, nefazodone,nortriptyline, olanzapine, oxazepam, paroxetine, pemoline, perphenazine,phenelzine, pimozide, pindolol, prazepam, propranolol, protriptyline,quetiapine, reboxetine, risperidone, selegiline, sertraline, sertindole,trifluoperazine, trimipramine, temazepam, thioridazine, topiramate,tranylcypromine, trazodone, triazolam, trihexyphenidyl, trimipramine,valproic acid or venlafaxine.
 87. The method of claim 76, wherein saidscore comprises a Clinical Global Improvement score.
 88. The method ofclaim 76, wherein said Clinical Global Improvement (CGI) score isselected from the group consisting of a CGI score of negative one (−1)indicating an adverse medication effect; a CGI score of zero (0)indicating no improvement; a CGI score of one (1) indicating minimal ormild improvement; a CGI score of two 2 indicating moderate improvement;and a CGI score of three (3) indicating marked improvement, includingcomplete absence of symptoms.